An Experimental Approach to Diagnose Covid-19 Using Optimized CNN

The outburst of novel corona viruses aggregated worldwide and has undergone severe trials to manage medical sector all over the world. A radiologist uses x-rays Computed Tomography (CT) scans analyze images through which existence virus is found. Therefore, imaging visualization systems contribute a dominant part in diagnosing process thereby assist experts take necessary precautions overcome these rigorous conditions. In this research, Multi-Objective Black Widow Optimization based Convolutional Neural Network (MBWO-CNN) method proposed diagnose classify covid-19 data. comprises four stages, preprocess data, attribute selection, tune parameters, Initially, are fed features selected using (CNN). Next, Multi-objective (MBWO) imparted finely hyper parameters CNN. Lastly, Extreme Learning Machine Auto Encoder (ELM-AE) used check further classification done data into respective classes. suggested MBWO-CNN model was evaluated for effectiveness by undergoing experiments outcomes attained were matched with outcome stationed prevailing methods. confirmed astonishing results ELM-AE achieving maximum accuracy 97.53%. efficacy validated observed that it yielded outstanding best suitable