Auxiliary diagnosis of depression in youth based on 3D-CBSResNet12 and multi-modal fusion strategy

Abstract

Major depressive disorder (MDD) is a mental illness with a significantly higher prevalence, especially among the youth population. In recent years, with the continuous innovation of deep learning technologies, the combination of medical imaging and computer-aided diagnosis has opened up new avenues and possibilities for studying a variety of diseases. However, many challenges are still faced in the research for the disease of depression. Among them, how to effectively improve the performance of network models under the condition of limited and small datasets is an urgent and crucial topic. Therefore, this paper proposes an auxiliary diagnosis method for depression, combining a lightweight deep learning network and a multi-modal feature fusion strategy, aiming to achieve more accurate and efficient depression diagnosis. Specifically, a lightweight dual-path architecture called 3D-CBSResNet12 is constructed, which utilizes multiple effective convolutional approaches as its core operations and employs a multi-modal feature fusion strategy to achieve more comprehensive and accurate classification of depression. Experimental results show that 3D-CBSResNet12 achieves significant performance improvement, fully validating its feasibility and effectiveness.