LungGANDetectAI: a GAN-augmented and attention-guided deep learning framework for accurate and explainable lung cancer detection.

Lung cancer is a leading cause of cancer-related death globally, and accurate diagnostic methods for early detection are crucial to enhance survival. The current diagnostic procedures and classical machine learning methods are helpful; however, modelling the structural variability of a lung nodule remains a long-standing and complex challenge. Although current deep learning approaches have achieved promising results, they still face challenges, including data scarcity (annotated datasets), bias (benign vs. malignant), overfitting (small sample sizes), and a black-box nature (non-interpretability), further hampering their use in clinical practice. These gaps also underscore the need for a robust, data-efficient, and explainable lung cancer detection framework. Thus, in this research, a GAN-augmented, attention-guided deep learning framework called LungGANDetectAI is proposed to address the shortcomings outlined above. It includes a GAN that generates synthetic CT images, alleviating class imbalance by augmenting samples of underrepresented classes with realistic images. Given the data size and imbalanced courses, this work proposed a classification task based on an attention model, GenAttnNet, that uses ResNet50 as its backbone and adds spatial and channel attention modules (SCAM) to enable the network to learn to focus on the most discriminative regions of the lung. Furthermore, Grad-CAM is applied to gradually reveal the clinical interpretability of the network’s decision-making. On the IQ-OTH/NCCD dataset, LungGANDetectAI achieves 98.4%, 98.2%, 98.3%, and 98.2% accuracy, precision, recall, and F1-score, respectively, only in malignant cases, effectively improving malignant case detection over baseline CNNs and state-of-the-art methods across extensive experiments. LungGANDetectAI is an accurate and interpretable decision-support tool that may serve as an early diagnostic aid for radiologists in lung cancer, leading to better patient-oriented services that improve outcomes.