The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are ins...The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are insufficientto prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious ExecutableDetection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)files in hosts using Windows operating systems through collecting PE headers and applying machine learningmechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031benign files and 179,071 malware samples from diverse sources to ensure the efficiency of RMED approach.The most effective PE headers that can highly differentiate between benign and malware files were selected totrain the model on 15 PE features to speed up the classification process and achieve real-time detection formalicious executables. The evaluation results showed that RMED succeeded in shrinking the classification timeto 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. Inconclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework thatleverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks.展开更多
Computer-aided design(CAD)software continues to be a crucial tool in digital twin application and manufacturing,facilitating the design of various products.We present a novel CAD generation method,an agent that constr...Computer-aided design(CAD)software continues to be a crucial tool in digital twin application and manufacturing,facilitating the design of various products.We present a novel CAD generation method,an agent that constructs the CAD sequences containing the sketch-and-extrude modelling operations efficiently and with high quality.Starting from the sketch and extrusion operation sequences,we utilise the transformer encoder to encode them into different disentangled codebooks to represent their distribution properties while considering their correlations.Then,a combination of auto-regressive and non-autoregressive samplers is trained to sample the code for CAD sequence con-struction.Extensive experiments demonstrate that our model generates diverse and high-quality CAD models.We also show some cases of real digital twin applications and indicate that our generated model can be used as the data source for the digital twin platform,exhibiting designers'potential.展开更多
BACKGROUND Artificial intelligence,such as convolutional neural networks(CNNs),has been used in the interpretation of images and the diagnosis of hepatocellular cancer(HCC)and liver masses.CNN,a machine-learning algor...BACKGROUND Artificial intelligence,such as convolutional neural networks(CNNs),has been used in the interpretation of images and the diagnosis of hepatocellular cancer(HCC)and liver masses.CNN,a machine-learning algorithm similar to deep learning,has demonstrated its capability to recognise specific features that can detect pathological lesions.AIM To assess the use of CNNs in examining HCC and liver masses images in the diagnosis of cancer and evaluating the accuracy level of CNNs and their performance.METHODS The databases PubMed,EMBASE,and the Web of Science and research books were systematically searched using related keywords.Studies analysing pathological anatomy,cellular,and radiological images on HCC or liver masses using CNNs were identified according to the study protocol to detect cancer,differentiating cancer from other lesions,or staging the lesion.The data were extracted as per a predefined extraction.The accuracy level and performance of the CNNs in detecting cancer or early stages of cancer were analysed.The primary outcomes of the study were analysing the type of cancer or liver mass and identifying the type of images that showed optimum accuracy in cancer detection.RESULTS A total of 11 studies that met the selection criteria and were consistent with the aims of the study were identified.The studies demonstrated the ability to differentiate liver masses or differentiate HCC from other lesions(n=6),HCC from cirrhosis or development of new tumours(n=3),and HCC nuclei grading or segmentation(n=2).The CNNs showed satisfactory levels of accuracy.The studies aimed at detecting lesions(n=4),classification(n=5),and segmentation(n=2).Several methods were used to assess the accuracy of CNN models used.CONCLUSION The role of CNNs in analysing images and as tools in early detection of HCC or liver masses has been demonstrated in these studies.While a few limitations have been identified in these studies,overall there was an optimal level of accuracy of the CNNs used in segmentation and classification of liver cancers images.展开更多
Melanoma is a skin disease with high mortality rate while earlydiagnoses of the disease can increase the survival chances of patients. Itis challenging to automatically diagnose melanoma from dermoscopic skinsamples. ...Melanoma is a skin disease with high mortality rate while earlydiagnoses of the disease can increase the survival chances of patients. Itis challenging to automatically diagnose melanoma from dermoscopic skinsamples. Computer-Aided Diagnostic (CAD) tool saves time and effort indiagnosing melanoma compared to existing medical approaches. In this background,there is a need exists to design an automated classification modelfor melanoma that can utilize deep and rich feature datasets of an imagefor disease classification. The current study develops an Intelligent ArithmeticOptimization with Ensemble Deep Transfer Learning Based MelanomaClassification (IAOEDTT-MC) model. The proposed IAOEDTT-MC modelfocuses on identification and classification of melanoma from dermoscopicimages. To accomplish this, IAOEDTT-MC model applies image preprocessingat the initial stage in which Gabor Filtering (GF) technique is utilized.In addition, U-Net segmentation approach is employed to segment the lesionregions in dermoscopic images. Besides, an ensemble of DL models includingResNet50 and ElasticNet models is applied in this study. Moreover, AOalgorithm with Gated Recurrent Unit (GRU) method is utilized for identificationand classification of melanoma. The proposed IAOEDTT-MC methodwas experimentally validated with the help of benchmark datasets and theproposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset.展开更多
In past decades,retinal diseases have become more common and affect people of all age grounds over the globe.For examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is nee...In past decades,retinal diseases have become more common and affect people of all age grounds over the globe.For examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is needed for automated diagnosis.To analyze the retinal malady,the system proposes a multiclass and multi-label arrangement method.Therefore,the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge,which tends to be time-consuming,vulnerable generalization ability,and unfeasible in massive datasets.Therefore,the automated diagnosis of multi-retinal diseases becomes essential,which can be solved by the deep learning(DL)models.With this motivation,this paper presents an intelligent deep learningbased multi-retinal disease diagnosis(IDL-MRDD)framework using fundus images.The proposed model aims to classify the color fundus images into different classes namely AMD,DR,Glaucoma,Hypertensive Retinopathy,Normal,Others,and Pathological Myopia.Besides,the artificial flora algorithm with Shannon’s function(AFA-SF)basedmulti-level thresholding technique is employed for image segmentation and thereby the infected regions can be properly detected.In addition,SqueezeNet based feature extractor is employed to generate a collection of feature vectors.Finally,the stacked sparse Autoencoder(SSAE)model is applied as a classifier to distinguish the input images into distinct retinal diseases.The efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset,comprising data instances from different classes.The experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963.展开更多
Osteosarcoma is one of the rare bone cancers that affect the individualsaged between 10 and 30 and it incurs high death rate. Early diagnosisof osteosarcoma is essential to improve the survivability rate and treatment...Osteosarcoma is one of the rare bone cancers that affect the individualsaged between 10 and 30 and it incurs high death rate. Early diagnosisof osteosarcoma is essential to improve the survivability rate and treatmentprotocols. Traditional physical examination procedure is not only a timeconsumingprocess, but it also primarily relies upon the expert’s knowledge.In this background, the recently developed Deep Learning (DL) models canbe applied to perform decision making. At the same time, hyperparameteroptimization of DL models also plays an important role in influencing overallclassification performance. The current study introduces a novel SymbioticOrganisms Search with Deep Learning-driven Osteosarcoma Detection andClassification (SOSDL-ODC) model. The presented SOSDL-ODC techniqueprimarily focuses on recognition and classification of osteosarcoma usinghistopathological images. In order to achieve this, the presented SOSDL-ODCtechnique initially applies image pre-processing approach to enhance the qualityof image. Also, MobileNetv2 model is applied to generate a suitable groupof feature vectors whereas hyperparameter tuning of MobileNetv2 modelis performed using SOS algorithm. At last, Gated Recurrent Unit (GRU)technique is applied as a classification model to determine proper class labels.In order to validate the enhanced osteosarcoma classification performance ofthe proposed SOSDL-ODC technique, a comprehensive comparative analysiswas conducted. The obtained outcomes confirmed the betterment of SOSDLODCapproach than the existing approaches as the former achieved a maximumaccuracy of 97.73%.展开更多
Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced stages.Early recognition of lung cancer is essential to increase the survival rate of persons a...Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced stages.Early recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare sector.Computer aided diagnosis(CAD)models can be designed to effectually identify and classify the existence of lung cancer using medical images.The recently developed deep learning(DL)models find a way for accurate lung nodule classification process.Therefore,this article presents a deer hunting optimization with deep convolutional neural network for lung cancer detection and classification(DHODCNNLCC)model.The proposed DHODCNN-LCC technique initially undergoes pre-processing in two stages namely contrast enhancement and noise removal.Besides,the features extraction process on the pre-processed images takes place using the Nadam optimizer with RefineDet model.In addition,denoising stacked autoencoder(DSAE)model is employed for lung nodule classification.Finally,the deer hunting optimization algorithm(DHOA)is utilized for optimal hyper parameter tuning of the DSAE model and thereby results in improved classification performance.The experimental validation of the DHODCNN-LCC technique was implemented against benchmark dataset and the outcomes are assessed under various aspects.The experimental outcomes reported the superior outcomes of the DHODCNN-LCC technique over the recent approaches with respect to distinct measures.展开更多
Recently,computer aided diagnosis(CAD)model becomes an effective tool for decision making in healthcare sector.The advances in computer vision and artificial intelligence(AI)techniques have resulted in the effective d...Recently,computer aided diagnosis(CAD)model becomes an effective tool for decision making in healthcare sector.The advances in computer vision and artificial intelligence(AI)techniques have resulted in the effective design of CAD models,which enables to detection of the existence of diseases using various imaging modalities.Oral cancer(OC)has commonly occurred in head and neck globally.Earlier identification of OC enables to improve survival rate and reduce mortality rate.Therefore,the design of CAD model for OC detection and classification becomes essential.Therefore,this study introduces a novel Computer Aided Diagnosis for OC using Sailfish Optimization with Fusion based Classification(CADOC-SFOFC)model.The proposed CADOC-SFOFC model determines the existence of OC on the medical images.To accomplish this,a fusion based feature extraction process is carried out by the use of VGGNet-16 and Residual Network(ResNet)model.Besides,feature vectors are fused and passed into the extreme learning machine(ELM)model for classification process.Moreover,SFO algorithm is utilized for effective parameter selection of the ELM model,consequently resulting in enhanced performance.The experimental analysis of the CADOC-SFOFC model was tested on Kaggle dataset and the results reported the betterment of the CADOC-SFOFC model over the compared methods with maximum accuracy of 98.11%.Therefore,the CADOC-SFOFC model has maximum potential as an inexpensive and non-invasive tool which supports screening process and enhances the detection efficiency.展开更多
Recently,computer assisted diagnosis(CAD)model creation has become more dependent on medical picture categorization.It is often used to identify several conditions,including brain disorders,diabetic retinopathy,and sk...Recently,computer assisted diagnosis(CAD)model creation has become more dependent on medical picture categorization.It is often used to identify several conditions,including brain disorders,diabetic retinopathy,and skin cancer.Most traditional CAD methods relied on textures,colours,and forms.Because many models are issue-oriented,they need a more substantial capacity to generalize and cannot capture high-level problem domain notions.Recent deep learning(DL)models have been published,providing a practical way to develop models specifically for classifying input medical pictures.This paper offers an intelligent beetle antenna search(IBAS-DTL)method for classifying medical images facilitated by deep transfer learning.The IBAS-DTL model aims to recognize and classify medical pictures into various groups.In order to segment medical pictures,the current IBASDTLM model first develops an entropy based weighting and first-order cumulative moment(EWFCM)approach.Additionally,the DenseNet-121 techniquewas used as a module for extracting features.ABASwith an extreme learning machine(ELM)model is used to classify the medical photos.A wide variety of tests were carried out using a benchmark medical imaging dataset to demonstrate the IBAS-DTL model’s noteworthy performance.The results gained indicated the IBAS-DTL model’s superiority over its pre-existing techniques.展开更多
Due to the rising occurrence of skin cancer and inadequate clinical expertise,it is needed to design Artificial Intelligence(AI)based tools to diagnose skin cancer at an earlier stage.Since massive skin lesion dataset...Due to the rising occurrence of skin cancer and inadequate clinical expertise,it is needed to design Artificial Intelligence(AI)based tools to diagnose skin cancer at an earlier stage.Since massive skin lesion datasets have existed in the literature,the AI-based Deep Learning(DL)modelsfind useful to differentiate benign and malignant skin lesions using dermoscopic images.This study develops an Automated Seeded Growing Segmentation with Optimal EfficientNet(ARGS-OEN)technique for skin lesion segmentation and classification.The proposed ASRGS-OEN technique involves the design of an optimal EfficientNet model in which the hyper-parameter tuning process takes place using the Flower Pollination Algorithm(FPA).In addition,Multiwheel Attention Memory Network Encoder(MWAMNE)based classification technique is employed for identifying the appropriate class labels of the dermoscopic images.A comprehensive simulation analysis of the ASRGS-OEN technique takes place and the results are inspected under several dimensions.The simulation results highlighted the supremacy of the ASRGS-OEN technique on the applied dermoscopic images compared to the recently developed approaches.展开更多
Medical image classification becomes a vital part of the design of computer aided diagnosis(CAD)models.The conventional CAD models are majorly dependent upon the shapes,colors,and/or textures that are problem oriented...Medical image classification becomes a vital part of the design of computer aided diagnosis(CAD)models.The conventional CAD models are majorly dependent upon the shapes,colors,and/or textures that are problem oriented and exhibited complementary in medical images.The recently developed deep learning(DL)approaches pave an efficient method of constructing dedicated models for classification problems.But the maximum resolution of medical images and small datasets,DL models are facing the issues of increased computation cost.In this aspect,this paper presents a deep convolutional neural network with hierarchical spiking neural network(DCNN-HSNN)for medical image classification.The proposed DCNN-HSNN technique aims to detect and classify the existence of diseases using medical images.In addition,region growing segmentation technique is involved to determine the infected regions in the medical image.Moreover,NADAM optimizer with DCNN based Capsule Network(CapsNet)approach is used for feature extraction and derived a collection of feature vectors.Furthermore,the shark smell optimization algorithm(SSA)based HSNN approach is utilized for classification process.In order to validate the better performance of the DCNN-HSNN technique,a wide range of simulations take place against HIS2828 and ISIC2017 datasets.The experimental results highlighted the effectiveness of the DCNN-HSNN technique over the recent techniques interms of different measures.Please type your abstract here.展开更多
Objective:To analyze the effect of using a problem-based(PBL)independent learning model in teaching cerebral ischemic stroke(CIS)first aid in emergency medicine.Methods:90 interns in the emergency department of our ho...Objective:To analyze the effect of using a problem-based(PBL)independent learning model in teaching cerebral ischemic stroke(CIS)first aid in emergency medicine.Methods:90 interns in the emergency department of our hospital from May 2022 to May 2023 were selected for the study.They were divided into Group A(45,conventional teaching method)and Group B(45 cases,PBL independent learning model)by randomized numerical table method to compare the effects of the two groups.Results:The teaching effect indicators and student satisfaction scores in Group B were higher than those in Group A(P<0.05).Conclusion:The use of the PBL independent learning model in the teaching of CIS first aid can significantly improve the teaching effect and student satisfaction.展开更多
The past decade has seen significant advances in endoscopic imaging and optical enhancements to aid early diagnosis.There is still a treatment gap due to the underdiagnosis of lesions of the oesophagus.Computer aided ...The past decade has seen significant advances in endoscopic imaging and optical enhancements to aid early diagnosis.There is still a treatment gap due to the underdiagnosis of lesions of the oesophagus.Computer aided diagnosis may play an important role in the coming years in providing an adjunct to endoscopists in the early detection and diagnosis of early oesophageal cancers,therefore curative endoscopic therapy can be offered.Research in this area of artificial intelligence is expanding and the future looks promising.In this review article we will review current advances in artificial intelligence in the oesophagus and future directions for development.展开更多
Oral Squamous Cell Carcinoma(OSCC)is a type of Head and Neck Squamous Cell Carcinoma(HNSCC)and it should be diagnosed at early stages to accomplish efficient treatment,increase the survival rate,and reduce death rate....Oral Squamous Cell Carcinoma(OSCC)is a type of Head and Neck Squamous Cell Carcinoma(HNSCC)and it should be diagnosed at early stages to accomplish efficient treatment,increase the survival rate,and reduce death rate.Histopathological imaging is a wide-spread standard used for OSCC detection.However,it is a cumbersome process and demands expert’s knowledge.So,there is a need exists for automated detection ofOSCC using Artificial Intelligence(AI)and Computer Vision(CV)technologies.In this background,the current research article introduces Improved Slime Mould Algorithm with Artificial Intelligence Driven Oral Cancer Classification(ISMA-AIOCC)model on Histopathological images(HIs).The presented ISMA-AIOCC model is aimed at identification and categorization of oral cancer using HIs.At the initial stage,linear smoothing filter is applied to eradicate the noise from images.Besides,MobileNet model is employed to generate a useful set of feature vectors.Then,Bidirectional Gated Recurrent Unit(BGRU)model is exploited for classification process.At the end,ISMA algorithm is utilized to fine tune the parameters involved in BGRU model.Moreover,ISMA algorithm is created by integrating traditional SMA and ChaoticOppositional Based Learning(COBL).The proposed ISMA-AIOCC model was validated for performance using benchmark dataset and the results pointed out the supremacy of ISMA-AIOCC model over other recent approaches.展开更多
Different teaching philosophies derive from different cultural background.There are many differences between American and Chinese teaching philosophies because of their different cultural background.Under the guidance...Different teaching philosophies derive from different cultural background.There are many differences between American and Chinese teaching philosophies because of their different cultural background.Under the guidance of different philosophies,there are different teaching styles between American and Chinese Classroom teaching.展开更多
文摘The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are insufficientto prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious ExecutableDetection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)files in hosts using Windows operating systems through collecting PE headers and applying machine learningmechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031benign files and 179,071 malware samples from diverse sources to ensure the efficiency of RMED approach.The most effective PE headers that can highly differentiate between benign and malware files were selected totrain the model on 15 PE features to speed up the classification process and achieve real-time detection formalicious executables. The evaluation results showed that RMED succeeded in shrinking the classification timeto 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. Inconclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework thatleverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks.
基金National Key Research and Development Program of China,Grant/Award Number:2022YFF0904303Beijing Science and Technology Planning Project,Grant/Award Number:Z221100006322003National Natural Science Foundation of China,Grant/Award Number:61932003。
文摘Computer-aided design(CAD)software continues to be a crucial tool in digital twin application and manufacturing,facilitating the design of various products.We present a novel CAD generation method,an agent that constructs the CAD sequences containing the sketch-and-extrude modelling operations efficiently and with high quality.Starting from the sketch and extrusion operation sequences,we utilise the transformer encoder to encode them into different disentangled codebooks to represent their distribution properties while considering their correlations.Then,a combination of auto-regressive and non-autoregressive samplers is trained to sample the code for CAD sequence con-struction.Extensive experiments demonstrate that our model generates diverse and high-quality CAD models.We also show some cases of real digital twin applications and indicate that our generated model can be used as the data source for the digital twin platform,exhibiting designers'potential.
基金Supported by the College of Medicine Research Centre,Deanship of Scientific Research,King Saud University,Riyadh,Saudi Arabia
文摘BACKGROUND Artificial intelligence,such as convolutional neural networks(CNNs),has been used in the interpretation of images and the diagnosis of hepatocellular cancer(HCC)and liver masses.CNN,a machine-learning algorithm similar to deep learning,has demonstrated its capability to recognise specific features that can detect pathological lesions.AIM To assess the use of CNNs in examining HCC and liver masses images in the diagnosis of cancer and evaluating the accuracy level of CNNs and their performance.METHODS The databases PubMed,EMBASE,and the Web of Science and research books were systematically searched using related keywords.Studies analysing pathological anatomy,cellular,and radiological images on HCC or liver masses using CNNs were identified according to the study protocol to detect cancer,differentiating cancer from other lesions,or staging the lesion.The data were extracted as per a predefined extraction.The accuracy level and performance of the CNNs in detecting cancer or early stages of cancer were analysed.The primary outcomes of the study were analysing the type of cancer or liver mass and identifying the type of images that showed optimum accuracy in cancer detection.RESULTS A total of 11 studies that met the selection criteria and were consistent with the aims of the study were identified.The studies demonstrated the ability to differentiate liver masses or differentiate HCC from other lesions(n=6),HCC from cirrhosis or development of new tumours(n=3),and HCC nuclei grading or segmentation(n=2).The CNNs showed satisfactory levels of accuracy.The studies aimed at detecting lesions(n=4),classification(n=5),and segmentation(n=2).Several methods were used to assess the accuracy of CNN models used.CONCLUSION The role of CNNs in analysing images and as tools in early detection of HCC or liver masses has been demonstrated in these studies.While a few limitations have been identified in these studies,overall there was an optimal level of accuracy of the CNNs used in segmentation and classification of liver cancers images.
基金supported by the MSIT (Ministry of Science and ICT),Korea,under the ICAN (ICT Challenge and Advanced Network of HRD)program (IITP-2022-2020-0-01832)supervised by the IITP (Institute of Information&Communications Technology Planning&Evaluation)and the Soonchunhyang University Research Fund.
文摘Melanoma is a skin disease with high mortality rate while earlydiagnoses of the disease can increase the survival chances of patients. Itis challenging to automatically diagnose melanoma from dermoscopic skinsamples. Computer-Aided Diagnostic (CAD) tool saves time and effort indiagnosing melanoma compared to existing medical approaches. In this background,there is a need exists to design an automated classification modelfor melanoma that can utilize deep and rich feature datasets of an imagefor disease classification. The current study develops an Intelligent ArithmeticOptimization with Ensemble Deep Transfer Learning Based MelanomaClassification (IAOEDTT-MC) model. The proposed IAOEDTT-MC modelfocuses on identification and classification of melanoma from dermoscopicimages. To accomplish this, IAOEDTT-MC model applies image preprocessingat the initial stage in which Gabor Filtering (GF) technique is utilized.In addition, U-Net segmentation approach is employed to segment the lesionregions in dermoscopic images. Besides, an ensemble of DL models includingResNet50 and ElasticNet models is applied in this study. Moreover, AOalgorithm with Gated Recurrent Unit (GRU) method is utilized for identificationand classification of melanoma. The proposed IAOEDTT-MC methodwas experimentally validated with the help of benchmark datasets and theproposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2021R1A2C1010362)the Soonchun-hyang University Research Fund.
文摘In past decades,retinal diseases have become more common and affect people of all age grounds over the globe.For examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is needed for automated diagnosis.To analyze the retinal malady,the system proposes a multiclass and multi-label arrangement method.Therefore,the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge,which tends to be time-consuming,vulnerable generalization ability,and unfeasible in massive datasets.Therefore,the automated diagnosis of multi-retinal diseases becomes essential,which can be solved by the deep learning(DL)models.With this motivation,this paper presents an intelligent deep learningbased multi-retinal disease diagnosis(IDL-MRDD)framework using fundus images.The proposed model aims to classify the color fundus images into different classes namely AMD,DR,Glaucoma,Hypertensive Retinopathy,Normal,Others,and Pathological Myopia.Besides,the artificial flora algorithm with Shannon’s function(AFA-SF)basedmulti-level thresholding technique is employed for image segmentation and thereby the infected regions can be properly detected.In addition,SqueezeNet based feature extractor is employed to generate a collection of feature vectors.Finally,the stacked sparse Autoencoder(SSAE)model is applied as a classifier to distinguish the input images into distinct retinal diseases.The efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset,comprising data instances from different classes.The experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963.
基金The Deanship of Scientific Research (DSR)at King Abdulaziz University (KAU),Jeddah,Saudi Arabia has funded this project,under grant no KEP-1-120-42.
文摘Osteosarcoma is one of the rare bone cancers that affect the individualsaged between 10 and 30 and it incurs high death rate. Early diagnosisof osteosarcoma is essential to improve the survivability rate and treatmentprotocols. Traditional physical examination procedure is not only a timeconsumingprocess, but it also primarily relies upon the expert’s knowledge.In this background, the recently developed Deep Learning (DL) models canbe applied to perform decision making. At the same time, hyperparameteroptimization of DL models also plays an important role in influencing overallclassification performance. The current study introduces a novel SymbioticOrganisms Search with Deep Learning-driven Osteosarcoma Detection andClassification (SOSDL-ODC) model. The presented SOSDL-ODC techniqueprimarily focuses on recognition and classification of osteosarcoma usinghistopathological images. In order to achieve this, the presented SOSDL-ODCtechnique initially applies image pre-processing approach to enhance the qualityof image. Also, MobileNetv2 model is applied to generate a suitable groupof feature vectors whereas hyperparameter tuning of MobileNetv2 modelis performed using SOS algorithm. At last, Gated Recurrent Unit (GRU)technique is applied as a classification model to determine proper class labels.In order to validate the enhanced osteosarcoma classification performance ofthe proposed SOSDL-ODC technique, a comprehensive comparative analysiswas conducted. The obtained outcomes confirmed the betterment of SOSDLODCapproach than the existing approaches as the former achieved a maximumaccuracy of 97.73%.
基金This work was funded by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,Under Grant No.(D-782-980-1443).
文摘Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced stages.Early recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare sector.Computer aided diagnosis(CAD)models can be designed to effectually identify and classify the existence of lung cancer using medical images.The recently developed deep learning(DL)models find a way for accurate lung nodule classification process.Therefore,this article presents a deer hunting optimization with deep convolutional neural network for lung cancer detection and classification(DHODCNNLCC)model.The proposed DHODCNN-LCC technique initially undergoes pre-processing in two stages namely contrast enhancement and noise removal.Besides,the features extraction process on the pre-processed images takes place using the Nadam optimizer with RefineDet model.In addition,denoising stacked autoencoder(DSAE)model is employed for lung nodule classification.Finally,the deer hunting optimization algorithm(DHOA)is utilized for optimal hyper parameter tuning of the DSAE model and thereby results in improved classification performance.The experimental validation of the DHODCNN-LCC technique was implemented against benchmark dataset and the outcomes are assessed under various aspects.The experimental outcomes reported the superior outcomes of the DHODCNN-LCC technique over the recent approaches with respect to distinct measures.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/142/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R151)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4310373DSR13This research project was supported by a grant from the Research Center of the Female Scientific and Medical Colleges,Deanship of Scientific Research,King Saud University.
文摘Recently,computer aided diagnosis(CAD)model becomes an effective tool for decision making in healthcare sector.The advances in computer vision and artificial intelligence(AI)techniques have resulted in the effective design of CAD models,which enables to detection of the existence of diseases using various imaging modalities.Oral cancer(OC)has commonly occurred in head and neck globally.Earlier identification of OC enables to improve survival rate and reduce mortality rate.Therefore,the design of CAD model for OC detection and classification becomes essential.Therefore,this study introduces a novel Computer Aided Diagnosis for OC using Sailfish Optimization with Fusion based Classification(CADOC-SFOFC)model.The proposed CADOC-SFOFC model determines the existence of OC on the medical images.To accomplish this,a fusion based feature extraction process is carried out by the use of VGGNet-16 and Residual Network(ResNet)model.Besides,feature vectors are fused and passed into the extreme learning machine(ELM)model for classification process.Moreover,SFO algorithm is utilized for effective parameter selection of the ELM model,consequently resulting in enhanced performance.The experimental analysis of the CADOC-SFOFC model was tested on Kaggle dataset and the results reported the betterment of the CADOC-SFOFC model over the compared methods with maximum accuracy of 98.11%.Therefore,the CADOC-SFOFC model has maximum potential as an inexpensive and non-invasive tool which supports screening process and enhances the detection efficiency.
文摘Recently,computer assisted diagnosis(CAD)model creation has become more dependent on medical picture categorization.It is often used to identify several conditions,including brain disorders,diabetic retinopathy,and skin cancer.Most traditional CAD methods relied on textures,colours,and forms.Because many models are issue-oriented,they need a more substantial capacity to generalize and cannot capture high-level problem domain notions.Recent deep learning(DL)models have been published,providing a practical way to develop models specifically for classifying input medical pictures.This paper offers an intelligent beetle antenna search(IBAS-DTL)method for classifying medical images facilitated by deep transfer learning.The IBAS-DTL model aims to recognize and classify medical pictures into various groups.In order to segment medical pictures,the current IBASDTLM model first develops an entropy based weighting and first-order cumulative moment(EWFCM)approach.Additionally,the DenseNet-121 techniquewas used as a module for extracting features.ABASwith an extreme learning machine(ELM)model is used to classify the medical photos.A wide variety of tests were carried out using a benchmark medical imaging dataset to demonstrate the IBAS-DTL model’s noteworthy performance.The results gained indicated the IBAS-DTL model’s superiority over its pre-existing techniques.
文摘Due to the rising occurrence of skin cancer and inadequate clinical expertise,it is needed to design Artificial Intelligence(AI)based tools to diagnose skin cancer at an earlier stage.Since massive skin lesion datasets have existed in the literature,the AI-based Deep Learning(DL)modelsfind useful to differentiate benign and malignant skin lesions using dermoscopic images.This study develops an Automated Seeded Growing Segmentation with Optimal EfficientNet(ARGS-OEN)technique for skin lesion segmentation and classification.The proposed ASRGS-OEN technique involves the design of an optimal EfficientNet model in which the hyper-parameter tuning process takes place using the Flower Pollination Algorithm(FPA).In addition,Multiwheel Attention Memory Network Encoder(MWAMNE)based classification technique is employed for identifying the appropriate class labels of the dermoscopic images.A comprehensive simulation analysis of the ASRGS-OEN technique takes place and the results are inspected under several dimensions.The simulation results highlighted the supremacy of the ASRGS-OEN technique on the applied dermoscopic images compared to the recently developed approaches.
文摘Medical image classification becomes a vital part of the design of computer aided diagnosis(CAD)models.The conventional CAD models are majorly dependent upon the shapes,colors,and/or textures that are problem oriented and exhibited complementary in medical images.The recently developed deep learning(DL)approaches pave an efficient method of constructing dedicated models for classification problems.But the maximum resolution of medical images and small datasets,DL models are facing the issues of increased computation cost.In this aspect,this paper presents a deep convolutional neural network with hierarchical spiking neural network(DCNN-HSNN)for medical image classification.The proposed DCNN-HSNN technique aims to detect and classify the existence of diseases using medical images.In addition,region growing segmentation technique is involved to determine the infected regions in the medical image.Moreover,NADAM optimizer with DCNN based Capsule Network(CapsNet)approach is used for feature extraction and derived a collection of feature vectors.Furthermore,the shark smell optimization algorithm(SSA)based HSNN approach is utilized for classification process.In order to validate the better performance of the DCNN-HSNN technique,a wide range of simulations take place against HIS2828 and ISIC2017 datasets.The experimental results highlighted the effectiveness of the DCNN-HSNN technique over the recent techniques interms of different measures.Please type your abstract here.
文摘Objective:To analyze the effect of using a problem-based(PBL)independent learning model in teaching cerebral ischemic stroke(CIS)first aid in emergency medicine.Methods:90 interns in the emergency department of our hospital from May 2022 to May 2023 were selected for the study.They were divided into Group A(45,conventional teaching method)and Group B(45 cases,PBL independent learning model)by randomized numerical table method to compare the effects of the two groups.Results:The teaching effect indicators and student satisfaction scores in Group B were higher than those in Group A(P<0.05).Conclusion:The use of the PBL independent learning model in the teaching of CIS first aid can significantly improve the teaching effect and student satisfaction.
文摘The past decade has seen significant advances in endoscopic imaging and optical enhancements to aid early diagnosis.There is still a treatment gap due to the underdiagnosis of lesions of the oesophagus.Computer aided diagnosis may play an important role in the coming years in providing an adjunct to endoscopists in the early detection and diagnosis of early oesophageal cancers,therefore curative endoscopic therapy can be offered.Research in this area of artificial intelligence is expanding and the future looks promising.In this review article we will review current advances in artificial intelligence in the oesophagus and future directions for development.
基金The work is supported by the Ministry of Science and Higher Education of the Russian Federation(Government Order FENU-2020-0022).
文摘Oral Squamous Cell Carcinoma(OSCC)is a type of Head and Neck Squamous Cell Carcinoma(HNSCC)and it should be diagnosed at early stages to accomplish efficient treatment,increase the survival rate,and reduce death rate.Histopathological imaging is a wide-spread standard used for OSCC detection.However,it is a cumbersome process and demands expert’s knowledge.So,there is a need exists for automated detection ofOSCC using Artificial Intelligence(AI)and Computer Vision(CV)technologies.In this background,the current research article introduces Improved Slime Mould Algorithm with Artificial Intelligence Driven Oral Cancer Classification(ISMA-AIOCC)model on Histopathological images(HIs).The presented ISMA-AIOCC model is aimed at identification and categorization of oral cancer using HIs.At the initial stage,linear smoothing filter is applied to eradicate the noise from images.Besides,MobileNet model is employed to generate a useful set of feature vectors.Then,Bidirectional Gated Recurrent Unit(BGRU)model is exploited for classification process.At the end,ISMA algorithm is utilized to fine tune the parameters involved in BGRU model.Moreover,ISMA algorithm is created by integrating traditional SMA and ChaoticOppositional Based Learning(COBL).The proposed ISMA-AIOCC model was validated for performance using benchmark dataset and the results pointed out the supremacy of ISMA-AIOCC model over other recent approaches.
文摘Different teaching philosophies derive from different cultural background.There are many differences between American and Chinese teaching philosophies because of their different cultural background.Under the guidance of different philosophies,there are different teaching styles between American and Chinese Classroom teaching.