DETECTION OF CHEST DISEASES IN RADIO GRAPHS USING DEEP LEARNING

One of the most widely used and easily accessible diagnostic methods for identifying a variety of pulmonary disorders is still chest radiography. However, interpreting chest radiographs accurately requires significant experience and time, which can lead to delays in diagnosis and treatment. To enhance the precision and efficiency of diagnosis, we present a deep learning-based method for the automated identification of chest disorders in radiographs. Our approach analyzes chest radiographs and detects anomalies indicative of various pulmonary diseases using convolutional neural networks (CNNs), a type of deep learning model well-suited for image recognition tasks. We utilize a large collection of annotated chest radiographs covering a wide range of pathological conditions, including pulmonary edema, lung cancer, pneumonia, and tuberculosis.