Classification of CT Images of COVID-19 Infected Lungs Based on Feature Fusion
This paper proposes a CT image classification method for COVID-19-infected lungs based on transfer learning and feature fusion,aiming to improve the accuracy and speed of image classification in the face of the difficulty in collecting a large number of well-annotated medical images.By using preprocessing and data enhancement techniques to filter out useless features,the method employs an attention module to better extract deep-level feature information,and uses a binary Focal Loss function to address the problem of imbalanced dataset distribution.Experimental results show that the proposed method achieves an image classification accuracy of 97.79％,representing an improvement in accuracy of 2.61％ and 1.81％compared to single models,effectively improving the accuracy of COVID-19-infected lung CT image classification.The classification model also exhibits good generalization performance,providing effective support for improving the accuracy of medical image classification.
deep learningtransfer learningfeature fusionimage classificationlung CT rec-ognition
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