Performance Analysis for Different Optimizers on the CNN Model for COVID-19 Disease Prediction Based on Chest X-Ray Images

Abstract

This paper analyzes the performance of different optimizers on the Convolutional Neural Network (CNN) model for COVID-19 disease prediction based on Chest X-ray images. The novel coronavirus known as ‘COVID-19’ or ‘Corona Virus Disease 2019’ has become a severe problem for the world community. Reverse Transcription Polymerase Chain Reaction (RT-PCR) can be known as a significant test used to diagnose COVID-19. However, some of the results of these tests were reported as false-negative, and healthcare facilities face limitations on RT-PCR tests since they are costly, complicated, less optimal of sensitivity, and time-consuming. Perceiving these limitations, detection and the classification of COVID-19 using chest X-ray images can be more accurate, faster, and less expensive when considering RT-PCR tests since X-ray imagery is one of the standard methods that has been used for several decades in medical diagnosis. Diagnosis of COVID-19 related to the radiological manifestations using chest X-ray images is unfamiliar since it is a new experience for many experts. The manual investigation is challenging and requires expertise radiologists. Therefore, a more robust 17 layered CNN model is carried out hereafter doing an experimental analysis on five different optimizers such as Stochastic Gradient Descent (SGD), Adaptive Gradient Descent (Adagrad), Adadelta, Root Mean Square Propagation (RMSprop), and Adaptive Moment Estimation (Adam) for the detection of COVID-19 disease based on chest X-ray images. The chest X-ray images under COVID-19 and normal were collected from a multi-class dataset in the Kaggle repository. The proposed model outperformed a training accuracy of 99% and a validation accuracy of 99% with the optimizer Adam along with max-pooling.