Optimizing non small cell lung cancer detection with convolutional neural networks and differential augmentation

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, with early detection being critical to improving patient outcomes. Recent advancements in deep learning have shown promise in enhancing diagnostic accuracy, particularly through the use of Convolutional Neural Networks (CNNs). This study proposes the integration of Differential Augmentation (DA) with CNNs to address the critical challenge of memory overfitting, a limitation that hampers the generalization of models to unseen data. By introducing targeted augmentation strategies, such as adjustments in hue, brightness, saturation, and contrast, the CNN + DA model diversifies training data and enhances its robustness. The research…