MCFCN: Multi-scale capsule-weighted fusion classification network for lung disease classification based on chest CT scans

Aim and scope: This paper aims to propose a Multi-scale Capsule-weighted Fusion Classification Network (MCFCN), a classification model for automatic diagnosis of lung lesions by CT scanning. Background: The automatic diagnosis of lung lesions based on chest CT scans plays a crucial role in assisting doctors to identify suspicious cases quickly and accurately. However, existing methods struggle to differentiate lesions with similar morphologies, and current feature extraction techniques lack the ability to effectively highlight small-scale targets in a large-scale environment, leading to incomplete extraction of subtle features and ultimately compromising the classification performance. Method: The MCFCN employs a dynamic routing clustering algorithm to emphasize small-scale features, preventing feature loss. Additionally, a scale difference fusion network is utilized to extract precise position scaling parameters by incorporating weighted fusion of information from different scales. Results: MCFCN achieves an accuracy of 99.41% for COVID-19 classification, 93.33% for CAP classification, and 100% for Normal classification, with an overall accuracy of 98.36%. Conclusion: Experimental results on the target dataset demonstrate that MCFCN outperforms state-of-the-art methods. In the future, this model can be further explored and optimized to enhance its application value in clinical practice