In healthcare sector,image classification is one of the crucial problems that impact the quality output from image processing domain.The purpose of image classification is to categorize different healthcare images under...In healthcare sector,image classification is one of the crucial problems that impact the quality output from image processing domain.The purpose of image classification is to categorize different healthcare images under various class labels which in turn helps in the detection and management of diseases.Magnetic Resonance Imaging(MRI)is one of the effective non-invasive strate-gies that generate a huge and distinct number of tissue contrasts in every imaging modality.This technique is commonly utilized by healthcare professionals for Brain Tumor(BT)diagnosis.With recent advancements in Machine Learning(ML)and Deep Learning(DL)models,it is possible to detect the tumor from images automatically,using a computer-aided design.The current study focuses on the design of automated Deep Learning-based BT Detection and Classification model using MRI images(DLBTDC-MRI).The proposed DLBTDC-MRI techni-que aims at detecting and classifying different stages of BT.The proposed DLBTDC-MRI technique involves medianfiltering technique to remove the noise and enhance the quality of MRI images.Besides,morphological operations-based image segmentation approach is also applied to determine the BT-affected regions in brain MRI image.Moreover,a fusion of handcrafted deep features using VGGNet is utilized to derive a valuable set of feature vectors.Finally,Artificial Fish Swarm Optimization(AFSO)with Artificial Neural Network(ANN)model is utilized as a classifier to decide the presence of BT.In order to assess the enhanced BT classification performance of the proposed model,a comprehensive set of simulations was performed on benchmark dataset and the results were vali-dated under several measures.展开更多
Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(MRI).An automated brain cancer classification using computer a...Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(MRI).An automated brain cancer classification using computer aided diagnosis(CAD)models can be designed to assist radiologists.With the recent advancement in computer vision(CV)and deep learning(DL)models,it is possible to automatically detect the tumor from images using a computer-aided design.This study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features(HGSO-FHDF)technique for brain cancer classification.The proposed HGSO-FHDF technique aims for detecting and classifying different stages of brain tumors.The proposed HGSO-FHDF technique involves Gabor filtering(GF)technique for removing the noise and enhancing the quality of MRI images.In addition,Tsallis entropy based image segmentation approach is applied to determine injured brain regions in the MRI image.Moreover,a fusion of handcrafted with deep features using Residual Network(ResNet)is utilized as feature extractors.Finally,HGSO algorithm with kernel extreme learning machine(KELM)model was utilized for identifying the presence of brain tumors.For examining the enhanced brain tumor classification performance,a comprehensive set of simulations take place on the BRATS 2015 dataset.展开更多
Histopathology is the investigation of tissues to identify the symptom of abnormality.The histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining th...Histopathology is the investigation of tissues to identify the symptom of abnormality.The histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining them.The investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s knowledge.At the same time,deep learning(DL)techniques are able to derive features,extract data,and learn advanced abstract data representation.With this view,this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification(EHCDL-HIC)model.The proposed EHCDLHIC technique initially performs Weiner filtering based noise removal technique.Once the images get smoothened,an ensemble of deep features and local binary pattern(LBP)features are extracted.For the classification process,the bidirectional gated recurrent unit(BGRU)model can be employed.At the final stage,the bacterial foraging optimization(BFO)algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance,shows the novelty of the work.For validating the enhanced execution of the proposed EHCDL-HIC method,a set of simulations is performed.The experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%.Therefore,the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.展开更多
Gold clay is a crafting medium consisting of gold particles mixed with an organic binder and water for making jewelry or decoration.The clay can be shaped by hand,textured,carved,formed or using molds.After drying and...Gold clay is a crafting medium consisting of gold particles mixed with an organic binder and water for making jewelry or decoration.The clay can be shaped by hand,textured,carved,formed or using molds.After drying and burning,the organic binder and water were decomposed and the gold particles were transformed to its final metal state.Although,gold clay is very expensive,it is useful to decorate the silver clay designed jewelry or small sculptures.In this research,nano-microplate gold and specific organic binder was used for producing nano-microplate gold clay.The objectives of this research are to study binder's type and ratios for optimum producing gold clay,and to study the heating condition for making silver and gold clay jewelry.The result showed that the clay can be fired with heating temperature at 900°C for an hour by electric kiln.The physical properties of the gold clay at different heating temperatures were determined.Furthermore,prototype of jewelry using the clay was展开更多
目的:观察手工训练对已有分离运动的脑卒中偏瘫患者上肢及手功能的疗效。方法:48例已有分离运动的脑卒中偏瘫患者随机分为实验组和对照组,两组均进行常规的康复治疗,实验组每天进行30min手工训练,对照组每天进行30min传统的作业治疗,两...目的:观察手工训练对已有分离运动的脑卒中偏瘫患者上肢及手功能的疗效。方法:48例已有分离运动的脑卒中偏瘫患者随机分为实验组和对照组,两组均进行常规的康复治疗,实验组每天进行30min手工训练,对照组每天进行30min传统的作业治疗,两组患者每周进行6次治疗,共4周,治疗前后两组分别进行Fug1-Meyer量表上肢部分(Fug1-Meyer assessment of upper extremity,FMA-UE)、上肢简易运动功能评分(simple test for evaluating hand function,STEF)、改良Barthel指数评分(modified Barthel index,MBI),比较两组治疗效果。结果:两组患者治疗4周后,其FMA-UE、STEF、MBI评分与治疗前相比差异有显著性意义(P<0.05),且实验组FMA-UE(48.45±3.93)、STEF(68.45±10.76)、MBI(71.87±8.69)与对照组FMA-UE(46.54±1.97)、STEF(59.16±8.37)、MBI(66.67±8.29)相比差异有显著性意义(P<0.05)。结论:系统的手工训练可明显改善已有分离运动的脑卒中偏瘫患者上肢及手的运动功能,提高其日常生活能力。展开更多
基金supported through the Annual Funding track by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[Project No.AN000684].
文摘In healthcare sector,image classification is one of the crucial problems that impact the quality output from image processing domain.The purpose of image classification is to categorize different healthcare images under various class labels which in turn helps in the detection and management of diseases.Magnetic Resonance Imaging(MRI)is one of the effective non-invasive strate-gies that generate a huge and distinct number of tissue contrasts in every imaging modality.This technique is commonly utilized by healthcare professionals for Brain Tumor(BT)diagnosis.With recent advancements in Machine Learning(ML)and Deep Learning(DL)models,it is possible to detect the tumor from images automatically,using a computer-aided design.The current study focuses on the design of automated Deep Learning-based BT Detection and Classification model using MRI images(DLBTDC-MRI).The proposed DLBTDC-MRI techni-que aims at detecting and classifying different stages of BT.The proposed DLBTDC-MRI technique involves medianfiltering technique to remove the noise and enhance the quality of MRI images.Besides,morphological operations-based image segmentation approach is also applied to determine the BT-affected regions in brain MRI image.Moreover,a fusion of handcrafted deep features using VGGNet is utilized to derive a valuable set of feature vectors.Finally,Artificial Fish Swarm Optimization(AFSO)with Artificial Neural Network(ANN)model is utilized as a classifier to decide the presence of BT.In order to assess the enhanced BT classification performance of the proposed model,a comprehensive set of simulations was performed on benchmark dataset and the results were vali-dated under several measures.
基金This research work was funded by Institutional fund projects under grant no.(IFPHI-180-612-2020)Therefore,the authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(MRI).An automated brain cancer classification using computer aided diagnosis(CAD)models can be designed to assist radiologists.With the recent advancement in computer vision(CV)and deep learning(DL)models,it is possible to automatically detect the tumor from images using a computer-aided design.This study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features(HGSO-FHDF)technique for brain cancer classification.The proposed HGSO-FHDF technique aims for detecting and classifying different stages of brain tumors.The proposed HGSO-FHDF technique involves Gabor filtering(GF)technique for removing the noise and enhancing the quality of MRI images.In addition,Tsallis entropy based image segmentation approach is applied to determine injured brain regions in the MRI image.Moreover,a fusion of handcrafted with deep features using Residual Network(ResNet)is utilized as feature extractors.Finally,HGSO algorithm with kernel extreme learning machine(KELM)model was utilized for identifying the presence of brain tumors.For examining the enhanced brain tumor classification performance,a comprehensive set of simulations take place on the BRATS 2015 dataset.
文摘Histopathology is the investigation of tissues to identify the symptom of abnormality.The histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining them.The investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s knowledge.At the same time,deep learning(DL)techniques are able to derive features,extract data,and learn advanced abstract data representation.With this view,this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification(EHCDL-HIC)model.The proposed EHCDLHIC technique initially performs Weiner filtering based noise removal technique.Once the images get smoothened,an ensemble of deep features and local binary pattern(LBP)features are extracted.For the classification process,the bidirectional gated recurrent unit(BGRU)model can be employed.At the final stage,the bacterial foraging optimization(BFO)algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance,shows the novelty of the work.For validating the enhanced execution of the proposed EHCDL-HIC method,a set of simulations is performed.The experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%.Therefore,the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.
文摘Gold clay is a crafting medium consisting of gold particles mixed with an organic binder and water for making jewelry or decoration.The clay can be shaped by hand,textured,carved,formed or using molds.After drying and burning,the organic binder and water were decomposed and the gold particles were transformed to its final metal state.Although,gold clay is very expensive,it is useful to decorate the silver clay designed jewelry or small sculptures.In this research,nano-microplate gold and specific organic binder was used for producing nano-microplate gold clay.The objectives of this research are to study binder's type and ratios for optimum producing gold clay,and to study the heating condition for making silver and gold clay jewelry.The result showed that the clay can be fired with heating temperature at 900°C for an hour by electric kiln.The physical properties of the gold clay at different heating temperatures were determined.Furthermore,prototype of jewelry using the clay was
文摘目的:观察手工训练对已有分离运动的脑卒中偏瘫患者上肢及手功能的疗效。方法:48例已有分离运动的脑卒中偏瘫患者随机分为实验组和对照组,两组均进行常规的康复治疗,实验组每天进行30min手工训练,对照组每天进行30min传统的作业治疗,两组患者每周进行6次治疗,共4周,治疗前后两组分别进行Fug1-Meyer量表上肢部分(Fug1-Meyer assessment of upper extremity,FMA-UE)、上肢简易运动功能评分(simple test for evaluating hand function,STEF)、改良Barthel指数评分(modified Barthel index,MBI),比较两组治疗效果。结果:两组患者治疗4周后,其FMA-UE、STEF、MBI评分与治疗前相比差异有显著性意义(P<0.05),且实验组FMA-UE(48.45±3.93)、STEF(68.45±10.76)、MBI(71.87±8.69)与对照组FMA-UE(46.54±1.97)、STEF(59.16±8.37)、MBI(66.67±8.29)相比差异有显著性意义(P<0.05)。结论:系统的手工训练可明显改善已有分离运动的脑卒中偏瘫患者上肢及手的运动功能,提高其日常生活能力。
