Optimized Convolutional Neural Network for Automatic Detection of COVID-19  

作　　者：K.Muthumayil M.Buvana K.R.Sekar Adnen El Amraoui Issam Nouaouri Romany F.Mansour 

机构地区：[1]Department of Information Technology,PSNA College of Engineering and Technology,Dindigul,624622,Tamilnadu,India [2]Department of Computer Science&Engineering,PSNA College of Engineering and Technology,Dindigul,624622,India [3]School of Computing,SASTRA Deemed University,Thanjavur,613401,India [4]Univ.Artois,U.R.3926,Laboratoire de Génie Informatique et d’Automatique de l’Artois(LGI2A),F-62400,Béthune,France [5]Department of Mathematics,Faculty of Science,New Valley University,El-Kharga,72511,Egypt

出　　处：《Computers, Materials & Continua》2022年第1期1159-1175,共17页计算机、材料和连续体（英文）

摘　　要：The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe.Radiologists use X-Rays or Computed Tomography(CT)images to confirm the presence of COVID-19.So,image processing techniques play an important role in diagnostic procedures and it helps the healthcare professionals during critical times.The current research work introduces Multi-objective Black Widow Optimization(MBWO)-based Convolutional Neural Network i.e.,MBWOCNN technique for diagnosis and classification of COVID-19.MBWOCNN model involves four steps such as preprocessing,feature extraction,parameter tuning,and classification.In the beginning,the input images undergo preprocessing followed by CNN-based feature extraction.Then,Multi-objective Black Widow Optimization(MBWO)technique is applied to fine tune the hyperparameters of CNN.Finally,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 labels.The proposed MBWO-CNN model was validated experimentally and the results obtained were compared with the results achieved by existing techniques.The 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 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]