LungX-Net: Lung Cancer Diagnosis from CT and Histopathological Images via Attention Based Multi-Level Feature Fusion Network

Lung cancer ranks among the top causes of cancer mortality worldwide. However, early detection can significantly boost survival rates, potentially increasing them by as much as 70%. While deep learning algorithms have shown promise in detecting lung cancer from CT scans, current methods are often inefficient, and single-model approaches struggle with complex medical images. Leveraging diverse model features can enhance accuracy and simplify detection. This paper introduces LungX-Net, an innovative and cost-effective deep learning model aimed at improving lung cancer diagnostic accuracy using CT and histopathological images. The proposed model unifies two networks into a unified, trainable feature extraction architecture, employing model compression to preserve essential core blocks. This approach enhances the model’s ability to propagate strong and robust features while significantly reducing computational resource requirements. The model also integrates a Convolutional Block Attention Module (CBAM) to enhance feature extraction by emphasizing the most important regions, which further improves overall accuracy. The integration of these techniques optimizes diagnostic performance while strategically minimizing complexity, making LungX-Net a powerful tool for efficient and precise lung cancer diagnosis. The proposed LungX-Net was evaluated on both CT and histopathological datasets, achieving a recognition accuracy of 99.09% on the CT dataset and 99.30% on the histopathological dataset. LungX-Net demonstrated superior performance compared to previous approaches, achieving greater accuracy with fewer parameters and faster testing times, highlighting its efficiency and cost-effectiveness.