Covid-19 detection from X-ray images using Customized Convolutional Neural Network

ABSTRACT

COVID-19 continues to have a devastating impact on the lives of people all over the world. Various new technologies arose in the research environment to assist mankind in surviving and living a better life. It is important to screen the infected patients in a timely and cost-effective manner to combat this disease and avoid its transmission. To achieve this aim, detection of Covid-19 from radiological evaluation of chest x-ray images using deep learning algorithms is less expensive and easily available option as it ensures fast and efficient diagnosis of the disease. Therefore, this paper presents a novel customized convolutional neural network (CNN) approach for the detection of COVID-19 from chest x-ray images. The performance of the proposed model is evaluated on three different size datasets, created from publicly available datasets. Experimental results show that the proposed model has better performance on Dataset 2. A very large increase or decrease of the number of samples in the dataset degrades the performance of the proposed model. The performance of the CNN model is compared with traditional pretrained networks namely VGG-16, VGG-19, ResNet-50 and Inception-V3. All the models show promising performance on dataset 2 which shows that optimum amount of data is enough for the model to lean features from the input data. Overall, the best validation accuracy of 97.78 was achieved by the proposed model on dataset 2. © 2022 IEEE.