Enhanced Lung Cancer Diagnosis using Deep Neural Networks

Utilizing an efficient deep learning architecture to ensure faster processing of CT scan images for real time detection of lung cancer which contributors to its fatalities, largely due to its diagnosis often occurring at advanced stages. While early detection dramatically improves the effectiveness of treatment, conventional diagnostic methods are expensive and require highly skilled expertise. The processing of these images for early lung cancer diagnosis is investigated in this work. A dataset comprising 12,000 images is employed. Design the solution to be easily integrated into existing medical imaging systems and accessible for use in hospitals or clinics. The proposed framework aims to deliver a diagnostic method that is faster, more accurate, and widely accessible. Results indicate that this technique holds great promise for improving early detection rates, potentially increasing survival rates among patients.