LUNG CANCER PREDICTION THROUGH DEEP LEARNING

Lung cancer remains a leading cause of cancer-related mortality worldwide, emphasizing the critical need for accurate prediction and early detection methods. In recent years, Convolutional Neural Network (CNN) models have emerged as powerful tools for medical image analysis, showing promising results in various diagnostic tasks. Building upon previous research utilizing Artificial Neural Network (ANN) models, this paper presents an in-depth investigation into the application of CNN models for lung cancer prediction using medical imaging data. Leveraging insights from previous ANN-based approaches, we propose novel CNN architectures and explore advanced techniques to enhance predictive performance. Through rigorous experimentation and evaluation, our study demonstrates the effectiveness of CNN models in accurately predicting lung cancer from computed tomography (CT) scan images. We also discuss the potential clinical implications and future directions for leveraging deep learning methods in lung cancer prediction and diagnosis.