Fault diagnosis using a probability least squares support vector classification machine

Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines is addressed by a proposed Probability Least Squares Support Vector Classification Machine (PLSSVCM). Samples that cannot be definitely determined as belonging to one class will be assigned to a class by the PLSSVCM based on a probability value. This gives the classification results both a qualitative explanation and a quantitative evaluation. Simulation results of a fault diagnosis show that the correct rate of the PLSSVCM is 100%. Even though samples are noisy, the PLSSVCM still can effectively realize multi-class fault diagnosis of a roller bearing. The generalization property of the PLSSVCM is better than that of a neural network and a LSSVCM.

Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification

Atherosclerosis diagnosis is an inarticulate and complicated cognitive process.Researches on medical diagnosis necessitate maximum accuracy and performance to make optimal clinical decisions.Since the medical diagnostic outcomes need to be prompt and accurate,the recently developed artificial intelligence(AI)and deep learning(DL)models have received considerable attention among research communities.This study develops a novel Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification(MDL-BADDC)model.The proposed MDL-BADDC technique encompasses several stages of operations such as pre-processing,feature selection,classification,and parameter tuning.Besides,the proposed MDL-BADDC technique designs a novel Quasi-Oppositional Barnacles Mating Optimizer(QOBMO)based feature selection technique.Moreover,the deep stacked autoencoder(DSAE)based classification model is designed for the detection and classification of atherosclerosis disease.Furthermore,the krill herd algorithm(KHA)based parameter tuning technique is applied to properly adjust the parameter values.In order to showcase the enhanced classification performance of the MDL-BADDC technique,a wide range of simulations take place on three benchmarks biomedical datasets.The comparative result analysis reported the better performance of the MDL-BADDC technique over the compared methods.

Classification and Diagnosis of Lymphoma’s Histopathological Images Using Transfer Learning

Current cancer diagnosis procedure requires expert knowledge and is time-consuming,which raises the need to build an accurate diagnosis support system for lymphoma identification and classification.Many studies have shown promising results using Machine Learning and,recently,Deep Learning to detect malignancy in cancer cells.However,the diversity and complexity of the morphological structure of lymphoma make it a challenging classification problem.In literature,many attempts were made to classify up to four simple types of lymphoma.This paper presents an approach using a reliable model capable of diagnosing seven different categories of rare and aggressive lymphoma.These Lymphoma types are Classical Hodgkin Lymphoma,Nodular Lymphoma Predominant,Burkitt Lymphoma,Follicular Lymphoma,Mantle Lymphoma,Large B-Cell Lymphoma,and T-Cell Lymphoma.Our proposed approach uses Residual Neural Networks,ResNet50,with a Transfer Learning for lymphoma’s detection and classification.The model used results are validated according to the performance evaluation metrics:Accuracy,precision,recall,F-score,and kappa score for the seven multi-classes.Our algorithms are tested,and the results are validated on 323 images of 224×224 pixels resolution.The results are promising and show that our used model can classify and predict the correct lymphoma subtype with an accuracy of 91.6%.

Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors(BT).A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate.The location and classification of BTs from huge medicinal images database,obtained from routine medical tasks with manual processes are a higher cost together in effort and time.An automatic recognition,place,and classifier process was desired and useful.This study introduces anAutomatedDeepResidualU-Net Segmentation with Classification model(ADRU-SCM)for Brain Tumor Diagnosis.The presentedADRUSCM model majorly focuses on the segmentation and classification of BT.To accomplish this,the presented ADRU-SCM model involves wiener filtering(WF)based preprocessing to eradicate the noise that exists in it.In addition,the ADRU-SCM model follows deep residual U-Net segmentation model to determine the affected brain regions.Moreover,VGG-19 model is exploited as a feature extractor.Finally,tunicate swarm optimization(TSO)with gated recurrent unit(GRU)model is applied as a classification model and the TSO algorithm effectually tunes theGRUhyperparameters.The performance validation of the ADRU-SCM model was tested utilizing FigShare dataset and the outcomes pointed out the better performance of the ADRU-SCM approach on recent approaches.

A comparative laboratory diagnosis of malaria:microscopy versus rapid diagnostic test kits

Objective:To compare the two methods of rapid diagnostic tests(RDTs)and microscopy in the diagnosis of malaria.Methods:RDTs and microscopy were carried out to diagnose malaria. Percentage malaria parasitaemia was calculated on thin films and all non-acute cases of plasmodiasis with less than 0.001%malaria parasitaemia were regarded as negative.Results were simply presented as percentage positive of the total number of patients under study.The results of RDTs were compared to those of microscopy while those of RDTs based on antigen were compared to those of RDTs based on antibody.Patients' follow-up was made for all cases.Results: All the 200 patients under present study tested positive to RDTs based on malaria antibodies(serum)method(100%).128 out of 200 tested positive to RDTs based on malaria antigen(whole blood)method(64%),while 118 out of 200 patients under present study tested positive to visual microscopy of Lieshman and diluted Giemsa(59%).All patients that tested positive to microscopy also tested positive to RDTs based on antigen.All patients on the second day of follow-up were non-febrile and had antimalaria drugs.Conclusions;We conclude based on the present study that the RDTs based on malaria antigen(whole blood)method is as specific as the traditional microscopy and even appears more sensitive than microscopy.The RDTs based on antibody(serum)method is unspecific thus it should not be encouraged.It is most likely that Africa being an endemic region,formation of certain levels of malaria antibody may not be uncommon.The present study also supports the opinion that a good number of febrile cases is not due to malaria. We support WHO's report on cost effectiveness of RDTs but,recommend that only the antigen based method should possibly,be adopted in Africa and other malaria endemic regions of the world.

Automated Colonic Polyp Detection and Classification Enabled Northern Goshawk Optimization with Deep Learning

The major mortality factor relevant to the intestinal tract is the growth of tumorous cells(polyps)in various parts.More specifically,colonic polyps have a high rate and are recognized as a precursor of colon cancer growth.Endoscopy is the conventional technique for detecting colon polyps,and considerable research has proved that automated diagnosis of image regions that might have polyps within the colon might be used to help experts for decreasing the polyp miss rate.The automated diagnosis of polyps in a computer-aided diagnosis(CAD)method is implemented using statistical analysis.Nowadays,Deep Learning,particularly throughConvolution Neural networks(CNN),is broadly employed to allowthe extraction of representative features.This manuscript devises a new Northern Goshawk Optimization with Transfer Learning Model for Colonic Polyp Detection and Classification(NGOTL-CPDC)model.The NGOTL-CPDC technique aims to investigate endoscopic images for automated colonic polyp detection.To accomplish this,the NGOTL-CPDC technique comprises of adaptive bilateral filtering(ABF)technique as a noise removal process and image pre-processing step.Besides,the NGOTL-CPDC model applies the Faster SqueezeNet model for feature extraction purposes in which the hyperparameter tuning process is performed using the NGO optimizer.Finally,the fuzzy Hopfield neural network(FHNN)method can be employed for colonic poly detection and classification.A widespread simulation analysis is carried out to ensure the improved outcomes of the NGOTL-CPDC model.The comparison study demonstrates the enhancements of the NGOTL-CPDC model on the colonic polyp classification process on medical test images.

Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification

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.

Symbiotic Organisms Search with Deep Learning Driven Biomedical Osteosarcoma Detection and Classification

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%.

Overview of peripheral arteriovenous malformations:From diagnosis to treatment methods

Based on the latest classification by the International Society for the Study of Vascular Anomalies in 2018,vascular malformations(VMs)can be categorized into simple,combined,VMs of major named vessels,and VMs associated with other anomalies.Simple VMs include lymphatic,venous,capillary,and arteriovenous malformations(AVMs).AVMs represent disorders of direct arteriovenous shunts caused by the absence of a capillary bed between the involved arteries and veins.This abnormal vascular communication causes arterial blood to accumulate in the venous vessels,thus resulting in venous hypertension and characteristic clinical manifestations,such as pulsation,tremors,and elevated temperature.AVMs can occur sporadically or as manifestations of syndromic lesions and are considered among the most complex and challenging VMs.The diagnosis and treatment of AVMs can vary depending on the lesion location and associated clinical symptoms,thus complicating their management.Herein,we discuss peripheral AVMs in terms of their clinical manifestations,imaging examinations,and staging systems to provide a comprehensive reference for the treatment,evaluation methods,and follow-up procedures for this vascular anomaly.

Hybrid Deep Learning Method for Diagnosis of Cucurbita Leaf Diseases

In agricultural engineering,the main challenge is on methodologies used for disease detection.The manual methods depend on the experience of the personal.Due to large variation in environmental condition,disease diagnosis and classification becomes a challenging task.Apart from the disease,the leaves are affected by climate changes which is hard for the image processing method to discriminate the disease from the other background.In Cucurbita gourd family,the disease severity examination of leaf samples through computer vision,and deep learning methodologies have gained popularity in recent years.In this paper,a hybrid method based on Convolutional Neural Network(CNN)is proposed for automatic pumpkin leaf image classification.The Proposed Denoising and deep Convolutional Neural Network(CNN)method enhances the Pumpkin Leaf Pre-processing and diagnosis.Real time data base was used for training and testing of the proposed work.Investigation on existing pre-trained network Alexnet and googlenet was investigated is done to evaluate the performance of the pro-posed method.The system and computer simulations were performed using Matlab tool.

Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

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.

Improved Model for Genetic Algorithm-Based Accurate Lung Cancer Segmentation and Classification

Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to patients.For lung cancer diagnosis,the computed tomography(CT)scan images are to be processed with image processing techniques and effective classification process is required for appropriate cancer diagnosis.In present scenario of medical data processing,the cancer detection process is very time consuming and exactitude.For that,this paper develops an improved model for lung cancer segmentation and classification using genetic algorithm.In the model,the input CT images are pre-processed with the filters called adaptive median filter and average filter.The filtered images are enhanced with histogram equalization and the ROI(Regions of Interest)cancer tissues are segmented using Guaranteed Convergence Particle Swarm Optimization technique.For classification of images,Probabilistic Neural Networks(PNN)based classification is used.The experimentation is carried out by simulating the model in MATLAB,with the input CT lung images LIDC-IDRI(Lung Image Database Consortium-Image Database Resource Initiative)benchmark Dataset.The results ensure that the proposed model outperforms existing methods with accurate classification results with minimal processing time.

Hyperparameter Tuned Deep Hybrid Denoising Autoencoder Breast Cancer Classification on Digital Mammograms

Breast Cancer(BC)is considered the most commonly scrutinized can-cer in women worldwide,affecting one in eight women in a lifetime.Mammogra-phy screening becomes one such standard method that is helpful in identifying suspicious masses’malignancy of BC at an initial level.However,the prior iden-tification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis.Deep learning(DL)techniques were broadly utilized for medical imaging applications,particularly breast mass classi-fication.The advancements in the DL field paved the way for highly intellectual and self-reliant computer-aided diagnosis(CAD)systems since the learning cap-ability of Machine Learning(ML)techniques was constantly improving.This paper presents a new Hyperparameter Tuned Deep Hybrid Denoising Autoenco-der Breast Cancer Classification(HTDHDAE-BCC)on Digital Mammograms.The presented HTDHDAE-BCC model examines the mammogram images for the identification of BC.In the HTDHDAE-BCC model,the initial stage of image preprocessing is carried out using an average median filter.In addition,the deep convolutional neural network-based Inception v4 model is employed to generate feature vectors.The parameter tuning process uses the binary spider monkey opti-mization(BSMO)algorithm.The HTDHDAE-BCC model exploits chameleon swarm optimization(CSO)with the DHDAE model for BC classification.The experimental analysis of the HTDHDAE-BCC model is performed using the MIAS database.The experimental outcomes demonstrate the betterments of the HTDHDAE-BCC model over other recent approaches.

Intelligent Beetle Antenna Search with Deep Transfer Learning Enabled Medical Image Classification Model

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.

Convolutional Neural Network-Based Classificationof Multiple Retinal Diseases Using Fundus Images

Use of deep learning algorithms for the investigation and analysis of medical images has emerged as a powerful technique.The increase in retinal dis-eases is alarming as it may lead to permanent blindness if left untreated.Automa-tion of the diagnosis process of retinal diseases not only assists ophthalmologists in correct decision-making but saves time also.Several researchers have worked on automated retinal disease classification but restricted either to hand-crafted fea-ture selection or binary classification.This paper presents a deep learning-based approach for the automated classification of multiple retinal diseases using fundus images.For this research,the data has been collected and combined from three distinct sources.The images are preprocessed for enhancing the details.Six layers of the convolutional neural network(CNN)are used for the automated feature extraction and classification of 20 retinal diseases.It is observed that the results are reliant on the number of classes.For binary classification(healthy vs.unhealthy),up to 100%accuracy has been achieved.When 16 classes are used(treating stages of a disease as a single class),93.3%accuracy,92%sensitivity and 93%specificity have been obtained respectively.For 20 classes(treating stages of the disease as separate classes),the accuracy,sensitivity and specificity have dropped to 92.4%,92%and 92%respectively.

Evaluation of a Direct Rapid Immunohistochemical Test (dRIT) for Rapid Diagnosis of Rabies in Animals and Humans

Presently the gold standard diagnostic technique for rabies is the direct immunofluorescence assay ( dFA) which is very expensive and requires a high level of expertise. There is a need for more economical and user friendly tests, particularly for use in developing countries. We have established one such test called the direct rapid immunohistochemical test (dRIT) for diagnosis of rabies using brain tissue. The test is based on capture of rabies nucleoprotein (N) antigen in brain smears using a cocktail of biotinylated monoclonal antibodies specific for the N protein and color development by streptavidin peroxidase-amino ethyl carbazole and counter staining with haematoxollin. The test was done in parallel with standard FAT dFA using 400 brain samples from different animals and humans. The rabies virus N protein appears under light microscope as reddish brown particles against a light blue background. There was 100 % correlation between the results obtained by the two tests. Also, interpretation of results by dRIT was easier and only required a light microscope. To conclude, this newly developed dRIT technique promises to be a simple, cost effective diagnostic tool for rabies and will have applicability in field conditions prevalent in developing countries.

A hybrid CNN-LSTM model for diagnosing rice nutrient levels at the rice panicle initiation stage

Nitrogen(N)and potassium(K)are two key mineral nutrient elements involved in rice growth.Accurate diagnosis of N and K status is very important for the rational application of fertilizers at a specific rice growth stage.Therefore,we propose a hybrid model for diagnosing rice nutrient levels at the early panicle initiation stage(EPIS),which combines a convolutional neural network(CNN)with an attention mechanism and a long short-term memory network(LSTM).The model was validated on a large set of sequential images collected by an unmanned aerial vehicle(UAV)from rice canopies at different growth stages during a two-year experiment.Compared with VGG16,AlexNet,GoogleNet,DenseNet,and inceptionV3,ResNet101 combined with LSTM obtained the highest average accuracy of 83.81%on the dataset of Huanghuazhan(HHZ,an indica cultivar).When tested on the datasets of HHZ and Xiushui 134(XS134,a japonica rice variety)in 2021,the ResNet101-LSTM model enhanced with the squeeze-and-excitation(SE)block achieved the highest accuracies of 85.38 and 88.38%,respectively.Through the cross-dataset method,the average accuracies on the HHZ and XS134 datasets tested in 2022 were 81.25 and 82.50%,respectively,showing a good generalization.Our proposed model works with the dynamic information of different rice growth stages and can efficiently diagnose different rice nutrient status levels at EPIS,which are helpful for making practical decisions regarding rational fertilization treatments at the panicle initiation stage.

Diagnostic Accuracy of IS6110 Insertion Gene, <i>Hsp65</i>, and Xpert MTB/RIF for Rapid Diagnosis of Pulmonary Tuberculosis

Purpose: To evaluate diagnostic accuracy of IS6110 insertion genes, hsp65, and Xpert MTB/RIF for rapid diagnosis of pulmonary tuberculosis. Methods: Sixty patients, medically reported HIV negative, clinically suspected of having pulmonary tuberculosis, were included in this study, and consented before enrolment. Sputum samples were gathered once, and tested by smear for Acid Fast Bacilli (AFB). Cultured in the Loewenstein-Jensen (LJ) medium for M. tuberculosis growth, M. tuberculosis DNA was detected by conventional PCR targeting IS6110, and hsp65 genes using specific primers, and automated nested real-time PCR targeting rpoB gene. Sensitivity, specificity and diagnostic accuracy were calculated for each method compared to culture. Results: Compared with culture as reference method, smear, IS6110, hsp65, and Xpert MTB/RIF had sensitivity 77.14%, 100%, 100%, and 100%, specificity 92%, 96%, 96%, and 96.97%, and diagnostic accuracy 83.33%, 98.33%, 98.33% and 98.21% respectively. Molecular diagnostic methods had the highest diagnostic accuracy, whereas smear had the lowest. No statistical significance, (p value > 0.05) was detected between the patients’ demographic data and the presence or absence of TB infection. Conclusion: The diagnostic accuracy that we got from the molecular methods, confirmed the diagnostic value of molecular detection of M. tuberculosis in pulmonary cases, supporting the application of automated and conventional PCR in rapid analysis. Smear could be more efficient when used for treatment monitoring. Combination between one-molecular techniques with smear as a routine method could be valid for rapid diagnosis of TB.

New supervised learning classifiers for structural damage diagnosis using time series features from a new feature extraction technique

The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduced to extract damage-sensitive features from auto-regressive models.This approach sets out to improve current feature extraction techniques in the context of time series modeling.The coefficients and residuals of the AR model obtained from the proposed approach are selected as the main features and are applied to the proposed supervised learning classifiers that are categorized as coefficient-based and residual-based classifiers.These classifiers compute the relative errors in the extracted features between the undamaged and damaged states.Eventually,the abilities of the proposed methods to localize and quantify single and multiple damage scenarios are verified by applying experimental data for a laboratory frame and a four-story steel structure.Comparative analyses are performed to validate the superiority of the proposed methods over some existing techniques.Results show that the proposed classifiers,with the aid of extracted features from the proposed feature extraction approach,are able to locate and quantify damage;however,the residual-based classifiers yield better results than the coefficient-based classifiers.Moreover,these methods are superior to some classical techniques.