A Deep Learning Approach for Lung Cancer Detection: AddressingOverfitting and Enhancing Model Generalization

Lung cancer is one of the leading causes of cancer-related deaths worldwide,emphasizing the need for accurate and early detection methods. This study investigates adeep learning-based framework for lung cancer prediction, employing a ConvolutionalNeural Network (CNN) architecture for imaging data analysis. The model achieved atraining accuracy of ۹۵% but demonstrated a testing accuracy of only ۷۵.۶%, indicatingchallenges with generalization and overfitting. Early stopping, learning rate adjustments,and dropout layers were used during training, but further improvements are necessary. Thefindings highlight the potential of CNNs for lung cancer detection while underlining theimportance of addressing data diversity, model complexity, and regularization to enhanceperformance. Future work will focus on integrating advanced techniques like transferlearning, data augmentation, and explainable AI (XAI) for robust and interpretable cancerdetection solutions.

Keywords:

Lung Cancer Detection , Machine Learning , OverfittingGeneralization , Regularization Techniques , Explainable AI (XAI) , Medical Imaging Analysis