Optimizing Convolutional Neural Networks with Transfer Learning for Making Classification Report in COVID-19 Chest X-Rays Scans

Abstract

The coronavirus disease (COVID-19) outbreak, which began in December 2019, has claimed numerous lives and impacted all aspects of human life. COVID-19 was deemed an outbreak by the World Health Organization (WHO) as time passed, putting a tremendous strain on substantially all countries, particularly those with poor health services and delayed reaction times. This recently identified virus is highly contagious. Controlling the rapid spread of this infection requires early detection of infected people through comprehensive screening. For COVID-19 viral diagnosis and follow-up, chest radiography imaging is an excellent tool. Deep learning (DL) has been used for a variety of healthcare purposes, including diabetic retinopathy detection, image classification, and thyroid diagnosis. DL is a useful strategy for combating the COVID-19 outbreak because there are so many streams of medical images (e.g., X-rays, CT, and MRI). In this study, we used the benchmark chest X-ray scan (CXRS) dataset for both COVID-19-infected and noninfected patients. We evaluate the results of DL-based convolutional neural network (CNN) models after preprocessing the scans and using data augmentation. Transfer learning (TL) is used to improve the algorithm’s classification performance for chest radiography imaging. Finally, features of the attention and feature interweave modules are combined to create a more accurate feature map. The architecture is trained for COVID-19 CXRS using CNN, and the newly generated feature layer is applied to TL architecture. The experimental results found that training enhances the CNN + TL algorithm’s ability to classify CXRS with an overall detection accuracy of 99.3%, precision (0.97), recall (0.98), f-measure (0.98), and receiver operating characteristic (ROC) curve (area = 0.97). The results show that further training improves the classification architecture’s performance by 99.3%.