Lung-AttNet: An Attention Mechanism-Based CNN Architecture for Lung Cancer Detection With Federated Learning

Lung cancer is one of the fatal diseases whose early diagnosis is essential to mitigate the death rate. Computed Tomography (CT) scans are widely used for lung cancer diagnosis, but manual interpretation by health professionals can lead to inconsistent results. To address this, we propose Lung-AttNet, a novel lightweight convolutional neural network (CNN) model enhanced with an attention mechanism. Lung-AttNet incorporates a convolutional block with a Lightweight Global Attention Module (LGAM) to effectively distinguish between lung cancer types. The convolutional block extracts both low- and high-dimensional features, while LGAM captures feature dependencies across channel and spatial dimensions. The model is evaluated using the Kaggle CT scan dataset, which includes four classes: adenocarcinoma, large cell carcinoma, squamous cell carcinoma, and normal. Extensive experiments, including ablation studies, 5-fold cross-validation, and explainable AI (XAI) techniques such as Grad-CAM and LIME, demonstrate that Lung-AttNet achieves an average accuracy of 91.5%. Furthermore, to address medical data sensitivity and privacy concerns, the model is deployed in a Federated Learning (FL) framework, where the global model is trained using weights from local models rather than sharing raw data. In the FL environment, Lung-AttNet achieves an accuracy of 92% with 2 and 3 clients, underscoring its robustness and adaptability for real-world applications.