An efficient, lightweight MobileNetV2-based fine-tuned model for COVID-19 detection using chest X-ray images.

ABSTRACT

In recent years, deep learning's identification of cancer, lung disease and heart disease, among others, has contributed to its rising popularity. Deep learning has also contributed to the examination of COVID-19, which is a subject that is currently the focus of considerable scientific debate. COVID-19 detection based on chest X-ray (CXR) images primarily depends on convolutional neural network transfer learning techniques. Moreover, the majority of these methods are evaluated by using CXR data from a single source, which makes them prohibitively expensive. On a variety of datasets, current methods for COVID-19 detection may not perform as well. Moreover, most current approaches focus on COVID-19 detection. This study introduces a rapid and lightweight MobileNetV2-based model for accurate recognition of COVID-19 based on CXR images; this is done by using machine vision algorithms that focused largely on robust and potent feature-learning capabilities. The proposed model is assessed by using a dataset obtained from various sources. In addition to COVID-19, the dataset includes bacterial and viral pneumonia. This model is capable of identifying COVID-19, as well as other lung disorders, including bacterial and viral pneumonia, among others. Experiments with each model were thoroughly analyzed. According to the findings of this investigation, MobileNetv2, with its 92% and 93% training validity and 88% precision, was the most applicable and reliable model for this diagnosis. As a result, one may infer that this study has practical value in terms of giving a reliable reference to the radiologist and theoretical significance in terms of establishing strategies for developing robust features with great presentation ability.