Automated Tuberculosis and Lung Cancer Detection Using Pre-trained SVM, KNN, and RNN for Enhanced Diagnostic Accuracy

This paper is based on Django-based web application to detect lung diseases like cancer and tuberculosis. It integrates machine learning and deep learning models, including CNN, SVM, KNN, and RNN, for accurate disease classification. The models are trained on X-ray and CT scan images to categorize them as normal, lung cancer, or tuberculosis. The web application allows medical professionals to upload images for real-time diagnosis. Features include automated classification, model training, and performance visualization through accuracy and loss graphs. The system is scalable, enabling continuous learning as new data is added. By combining advanced AI techniques, it enhances diagnostic efficiency and supports medical decision-making. The platform aims to improve early detection of lung diseases. It also contributes to research in medical image processing. The goal is to aid in timely diagnosis and improve patient outcomes. Here we get the accuracy of 84.56%.