Diagnosing Multiple Chest Diseases with Deep Learning: A Comprehensive Approach Using X-ray Images

Abstract

Recent research indicates that healthcare is a critical component of our daily lives, and the medical industry is developing advanced techniques for detecting illnesses as technology continues to evolve. The rapid spread of infectious diseases has a significant impact on people’s lives and has caused significant global problems. Unfortunately, the COVID-19 virus is one such disease that is often misdiagnosed as pneumonia or lung cancer. Deep learning technology has made remarkable advancements in identifying diseases from radiographic images such as CT scans, X-rays. Due to a shortage of resources for RT-PCR, early detection of illnesses is difficult. Therefore, chest X-rays can be used to detect severe disorders. This study focuses on the detection of diseases using X-ray image datasets for four conditions: COVID-19, lung cancer, Tuberculosis, and pneumonia. Various deep learning techniques, including VGG16, Densenet, Autoencoder, Resnet, and Convolutional Neural Networks (CNN) are used to identify these disorders. Ensemble learning is applied to diagnosing diseases.