Classification of recurrent depression using brain CT images through feature fusion

Objective: The objective of the study is to address the issues of high workload and low diagnostic efficiency in clinical medicine by employing computer-aided diagnostic analysis methods. The focus is on the identification of depressed patients using medical image data, particularly brain CT images. Methods: The study proposes a CT image classification method for identifying depression disorder based on deep learning theory. Key methods include migration learning and feature fusion. Preprocessing and data enhancement techniques are utilized to filter out unnecessary features. An attention module is employed to better extract deep feature information. Additionally, a binary classification focus loss function is applied to address the unbalanced distribution of the dataset. Results: Experimental results indicate that the proposed method achieves an image classification accuracy of 97.79%. Compared to a single model, there is an increase in accuracy by 2.61% and 1.81%, respectively. This improvement effectively enhances the classification accuracy of brain CT images with depressive disorders. The classification model also demonstrates better generalization performance. Conclusion: The study concludes that the proposed CT image classification method, incorporating migration learning, feature fusion, preprocessing, and attention modules, significantly improves the accuracy and classification speed of identifying depressive disorders in brain CT images. The model generalization performance is highlighted, providing effective support for the enhancement of medical image classification accuracy