Examine Lung Disorders and Disease Classification Using Advanced CNN Approach

Diseases have a significant impact on both the physical and mental well-being of individuals, arising primarily from four causes: infections, deficiencies, hereditary factors, and organ dysfunction. In our society today, it is the responsibility of medical professionals to accurately identify and classify diseases, offering appropriate medical interventions for either curing or managing the condition. While some diseases can be completely cured, there are chronic illnesses that persist but can be controlled to prevent further deterioration. Therefore, early detection and intervention are crucial in effectively addressing diseases. Moreover, the detection and classification of diseases rely heavily on cutting-edge methodologies like machine learning, deep learning, and artificial intelligence. This study specifically focuses on utilizing deep learning and image processing methods to classify lung disorders, aiming to assist healthcare experts in their diagnostic procedures. The first step involves collecting lung images from affected individuals for the purpose of classification. The Convolutional Neural Network (CNN) model is then trained using a specific dataset, with a primary emphasis on disease identification. Subsequently, the model is applied to classify new and previously unseen chest X-ray images from a separate test dataset. As a result, the proposed CNN model demonstrates the ability to categorize chest X-ray images into classes such as COVID-19, Pneumonia, Tuberculosis, and Normal, achieving an impressive 94% accuracy in the classification of lung diseases.