Abstract:

Artificial intelligence （AI）‑based diagnosis has become an important auxiliary method for detecting lung infections. However， most existing approaches rely on deep learning， which are often plagued by issues such as insufficient model stability， high complexity， and low accuracy. This paper presents a shallow model which incorporates a multi‑scale attention mechanism to achieve both high accuracy and a simple structure for diagnosing COVID‑19 from CT scans. Firstly， two datasets of COVID‑19 CT images are combined into a single dataset to address the issue of model instability caused by insufficient data. Secondly， by introducing multi‑scale attention（MA） in the final three layers of the shallow ResNet18 network， the model’s feature extraction capability is enhanced. Finally， classifier with three fully connected layers （CTFCL） is constructed to improve the classification performance of the model， thereby increasing the accuracy of lung CT classification. Experimental results show that the proposed model achieves an accuracy of 95.41%， outperforming networks such as ResNet50， ResNet101， VGG16， and DenseNet169. Furthermore， the model has only 12.24×10⁶ parameters， which is approximately 50% fewer than networks like ResNet50 and VGG16.

Key words: pneumonia, deep learning, multi-scale attention, CT, classifier