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Deep Optimal VGG16 Based COVID-19 Diagnosis model

Computers, materials & Continua 2022年第1期70卷 43-58页

作者： m.buvana K.muthumayil S.Senthil kumar Jamel Nebhen Sultan S.Alshamrani Ihsan Ali Department of Computer Science and Engineering PSNA College of Engineering and TechnologyDindigul624622India Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and TechnologyChennai600073India Prince Sattam Bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia Department of Information Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia

Coronavirus(COVID-19)outbreak was first identified in Wuhan,China in December *** was tagged as a pandemic soon by the WHO being a serious public medical *** spite of the fact that the virus can be diagnosed by qRT-PCR,COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray(CXR)and Computed Tomography(CT)*** this paper,the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various *** features like Speeded-Up Robust Features(SURF),Features from Accelerated Segment Test(FAST)and Scale-Invariant Feature Transform(SIFT)are used in the test images to detect the presence of *** optimal features are extracted from the images utilizing DeVGGCovNet(Deep optimal VGG16)model through optimal learning *** task is accomplished by exceptional mating conduct of Black Widow *** this strategy,cannibalism is *** this phase,fitness outcomes are rejected and are not satisfied by the proposed *** results acquired from real case analysis demonstrate the viability of DeVGGCovNet technique in settling true issues using obscure and testing ***16 model identifies the imagewhich has a place with which it is dependent on the distinctions in *** impact of the distinctions on labels during training stage is studied and predicted for test *** proposed model was compared with existing state-of-the-art models and the results from the proposed model for disarray grid estimates like Sen,Spec,Accuracy and F1 score were promising.

关键词： COVID 19 multi-feature extraction vgg16 optimal learning rate

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Optimized Convolutional Neural Network for Automatic Detection of COVID-19

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Computers, materials & Continua 2022年第1期70卷 1159-1175页

作者： K.muthumayil m.buvana K.R.Sekar Adnen El Amraoui Issam Nouaouri Romany F.mansour Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622TamilnaduIndia Department of Computer Science&Engineering PSNA College of Engineering and TechnologyDindigul624622India School of Computing SASTRA Deemed UniversityThanjavur613401India Univ.Artois U.R.3926Laboratoire de Génie Informatique et d’Automatique de l’Artois(LGI2A)F-62400BéthuneFrance Department of mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the *** use X-Rays or Computed Tomography(CT)images to confirm the presence of ***,image processing techniques play an important role in diagnostic procedures and it helps the healthcare professionals during critical *** current research work introduces multi-objective Black Widow Optimization(mBWO)-based Convolutional Neural Network i.e.,mBWOCNN technique for diagnosis and classification of *** model involves four steps such as preprocessing,feature extraction,parameter tuning,and *** the beginning,the input images undergo preprocessing followed by CNN-based feature ***,multi-objective Black Widow Optimization(mBWO)technique is applied to fine tune the hyperparameters of ***,Extreme Learning machine with autoencoder(ELm-AE)is applied as a classifier to confirm the presence of COVID-19 and classify the disease under different class *** proposed mBWO-CNN model was validated experimentally and the results obtained were compared with the results achieved by existing *** experimental results ensured the superior results of the ELm-AE model by attaining maximum classification performance with the accuracy of 96.43%.The effectiveness of the technique is proved through promising results and the model can be applied in diagnosis and classification of COVID-19.

关键词： COVID-19 classification CNN hyperparameter tuning black widow optimization

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