Classification of Respiratory Disorders Using Deep Learning

Global health is still being significantly impacted by the recent pandemic, coronavirus-2019 (COVID-19), quick testing, effective diagnosis, and treatment. The early detection of COVID-19 is crucial for effective treatment and avoiding strain on the healthcare system because it can cause severe pneumonia. In addition to COVID-19, other causes of pneumonia and tuberculosis (TB) present additional challenges for the medical system. According to Bernadeta Dadonaite and Max Roser, “2.5 million people died from pneumonia in 2019,” according to their article on Our World in Data, which was first published in November 2019. It is the leading cause of minor's death, accounting for nearly a third of all victims. The principal imaging modalities for identifying respiratory disorders are chest X-rays (CXR) and computed tomography (CT) scans. Despite being the most advanced standard, CT scans are more expensive, take longer, and expose the patient to a modest but considerable dosage of radiation. As a result, CXR has become increasingly often used as a first-line inquiry. In recent years, it has become clear that Computer Aided Designs (CAD) has flourished in the medical sector, particularly in the use of deep learning for the early detection of lung nodules, breast cancer, and other conditions using mammography. When applying Deep Learning (DL) techniques to medical images, expressive features are crucial. Most earlier algorithms that created CAD systems by analyzing images used hand-crafted features as a result. So hence, this study proposes a model to classify pneumonia based on the area of infection in lungs. This experimental work includes classification of pneumonia, covid pneumonia and TB pneumonia using Chest X-Rays Images.