Diagnosis of Lung Cancer Based on CT Scans Using Deep Learning and Web Application

The early detection of lung cancer, which is the second most lethal type of cancer worldwide, plays a vital role in improving patient survival rates. The research introduces a lung cancer detection system that operates independently through deep learning techniques which evaluate Computed Tomography (CT) scan images. The development of a Convolutional Neural Network (CNN) model enables automatic subject identification through CT image processing which produces normal and malignant output results. The system uses image preprocessing and segmentation together with deep learning methods to classify images which results in better diagnostic results. The web-based application receives the trained model through Python Flask framework which allows users to upload CT scan images for instant diagnostic results. The proposed CNN model achieved 94.7% accuracy, 92.1% precision, 90.5% recall, and 91.3% F1-score during the evaluation of the labeled CT scan dataset which demonstrated its ability to detect lung cancer patterns. The proposed system functions as a dependable computer-aided diagnostic solution which helps healthcare professionals identify lung cancer at an early stage. The deep learning model, when combined with a webbased interface, enhances real-time medical analysis through its ability to provide easier access and support larger operational scales.