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.
