Classification of Lung Disease with Recommendation using Deep Learning

The early diagnosis of lung diseases, including pneumonia, tuberculosis (TB), asthma, fibrosis, and COVID-19 is critical for effective treatment and management. With the emergence of COVID-19, distinguishing between pneumonia caused by the virus and other respiratory illnesses has become increasingly challenging. Medical imaging, particularly chest CT scans, plays a pivotal role in accurate diagnosis. In recent years, machine learning and image processing techniques have been employed to aid in the classification of lung diseases using various medical images such as X-rays, CT scans, and histopathology slides. Transfer learning, a powerful technique for addressing complex cognitive tasks, has shown promise in this domain. This study aims to utilize an Xception Model, a supervised neural network, to detect COVID-19 using CT scan images. Additionally, this project employs a Multi-class Xception model to differentiate between various lung diseases, including pneumonia, TB, influenza, cancer, and COVID-19. The methodology involves training the models on a dataset consisting of annotated medical images and evaluating their performance in disease classification. The results show that the proposed method improves accuracy in disease classification and provides valuable diagnostic insights for the diseases under study. The utilization of transfer learning with the Xception architecture enhances the efficacy of the models in accurately identifying lung diseases, including distinguishing COVID-19 from pneumonia and other respiratory conditions. Overall, this study contributes to the advancement of AI-based systems for aiding medical professionals in the early and accurate diagnosis of lung diseases.