Pulmonary Disease Multiclassification of Chest X-ray Images using Deep Learning Techniques

This research paper provides a novel approach for identifying lung illnesses using X-ray pictures, with an emphasis on distinguishing between five major respiratory ailments and a conventional lung classification. The proposed model evaluates X-ray images and produces exact classification results for pneumonia, COVID-19, and a typical lung using cutting-edge deep learning approaches such as Convolutional Neural Networks (CNNs), DenseNet, MobileNet, and Inception. Using a diverse and extensively annotated dataset, the model is subjected to rigorous training and validation techniques that maximize its ability to detect minute patterns that may be indicative of certain diseases. This will help to enhance automated diagnostic tools for prompt and effective medical interventions. This study investigates a novel use of X-ray imaging for the diagnosis of lung diseases, including Pneumonia, Covid 19 and a normal standard lung. Consider X-rays as unique superhero eyes that have the ability to peer into our chests. Computers have been trained by scientists to recognize lung diseases in patients based on X-ray images. Authors are assisting physicians in identifying and treating these lung issues more quickly by teaching computers to recognize these signs.