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 utilized multiple datasets, including the IQ-OTH/NCCD dataset, to evaluate the proposed model against existing state-of-the-art methods. Hyperparameter tuning was performed using Random Search to optimize parameters, further improving performance. The results revealed that the CNN + DA model achieved an accuracy of 98.78%, outperforming advanced models like DenseNet, ResNet, and EfficientNetB0, as well as hybrid approaches including ensemble models. Additionally, statistical analyses, including Tukey's HSD post-hoc tests, confirmed the significance of the model's superior performance. These findings suggest that the CNN + DA model effectively addresses the limitations of prior works by reducing overfitting and ensuring reliable generalization across diverse datasets. The study concludes that the novel CNN + DA architecture provides a robust, accurate, and computationally efficient framework for lung cancer detection, positioning it as a valuable tool for clinical applications and paving the way for future research in medical image diagnostics.