An Explainable and Lightweight Deep Learning Model with Attention Mechanism for Efficient Lung Disease Detection

COVID-19 has rapidly spread across the world as an extremely contagious disease. Therefore, early detection of the virus is essential to effectively control its transmission and prevent further spread. It is widely reported in the literature that convolutional neural networks (CNNs) are commonly utilized for COVID-19 detection. However, the majority of these models require substantial computational resources and possess a large number of parameters, rendering them less feasible for deployment on real-time devices. This study proposes a solution to address COVID-19 detection challenges by introducing LWIA-Net, a rapid and highly efficient lightweight CNN architecture. The LWIA-Net is equipped with channel attention squeeze and excitation (SE) blocks, as well as a naïve inception module, to enhance the network’s learning ability, reduce computational complexity, and maintain a high level of detection performance. By incorporating the naïve inception module, the LWIA-Net enables multi-level feature extraction, while the inclusion of the SE block helps focuses on informative channels. This integration contributes to the production of high-quality image features and effectively reduces redundancy. We performed thorough experiments using both our locally developed dataset and a publicly available dataset. The statistical analysis demonstrate that LWIA-Net achieves superior performance compared to pre-trained CNN architectures, despite its extremely lightweight architecture which demands low computational cost, memory space, and consists of only 1.11 million parameters. We conducted a comprehensive ablation study to gain a deeper understanding of the significance of each component and to verify their individual contributions to the LWIA-Net. Additionally, we utilized Grad-CAM analysis to confirm that the proposed model effectively identifies and emphasizes the most critical regions of interest. Experimental results have shown that LWIA-Net achieves remarkable accuracy rates of 94.45% and 97.56% on a local dataset and a public dataset. These findings suggest that LWIA-Net could potentially assist medical professionals in identifying patients with COVID-19 infection.