Mobile App for Multi-Disease Detection in Lungs Using CNN

Diseases of the lung like pneumonia, tuberculosis, and COPD are important global health concerns that commonly result in serious complications or death from delayed or incorrect diagnoses. Conventional approaches to diagnosis like chest X-rays and biopsies are largely dependent on skilled interpretation that is time- consuming and subject to human error. This work suggests the creation of a mobile app that utilizes Convolutional Neural Networks (CNNs) to automatically detect and classify various lung diseases based on chest X-ray images. The app shall be created as an API-based system to make it accessible and process data in real-time for medical practitioners. Using deep learning methods, the system seeks to improve diagnostic accuracy, minimize the need for human interpretation, and deliver fast insights into lung diseases. Early model training on pre-processed datasets has shown promising classification accuracy, especially between bacterial and viral pneumonia. The intended application will be a deployable and scalable solution, enabling early diagnosis and enhancing healthcare accessibility, particularly in resource-constrained environments.