A Novel Framework for Lung Disease Classification Using Multiscale Convolutional Neural Networks With an Integrated Dynamic Attention Mechanism

Abstract

Lung disease diagnosis remains a significant clinical challenge due to the similarity in radiological features across various conditions such as COPD, pneumonia, tuberculosis, COVID-19, and lung cancer. Manual interpretation of chest CT scans is time-consuming and subject to inter-observer variability, particularly in resource-limited settings. To address these challenges, this study proposes a novel deep learning framework Multiscale Convolutional Neural Networks with Attention Mechanism (MCNN-AM) for automated classification of lung diseases into six categories, including normal lungs. The model leverages multiscale convolutional layers to extract both localized and global features, enabling better discrimination between diseases with overlapping characteristics. A dynamic attention mechanism, comprising both spatial and channel attention modules, is integrated to emphasize disease-relevant regions and suppress background noise, enhancing the model's diagnostic focus. Additionally, depthwise separable convolutions are utilized to reduce computational complexity while preserving feature richness. The MCNN-AM model is trained and evaluated on publicly available datasets, comprising 6000 training images and 1200 testing images equally distributed across all classes. The model achieves a classification accuracy of 96.84%, outperforming state-of-the-art models such as ResNet50, DenseNet121, and InceptionV3 in terms of precision, recall, F1-score, sensitivity, and specificity. Ablation studies further validate the critical role of the attention modules in achieving high performance. These results demonstrate the potential of MCNN-AM as a reliable, scalable tool for computer-aided diagnosis of lung diseases.