Next-Gen Chest Cancer Detection using AI Techniques

The early detection of chest cancer is critical for treatment and cure and to improve survival rates. Conventional diagnostic methods, such as radiological-based methods and biopsies, have limitations including operator error, drawn-out time for diagnosis, and differences in interpretation. It is the goal of this project to design an AI-based technique to systematically improve the quality and speed of chest cancer diagnosis. Deep learning, particularly CNN and the other machine-learning algorithms, has been used to analyze the medical imaging modalities, namely x-ray, CT, and MRI scans. The videos are trained to identify very malignant or benign abnormalities, with very low associated false positives and false negatives. The integration of AI-based computer-aided detection (CAD) systems in this research would aim to automate diagnosis and assist radiologists in oncology decision-making. The AI-based detection would, therefore, empower cancer diagnostics to a new dimension of transformation, through speed, accuracy, and cost-effective methods to health practitioners.