Inadequate dataset learning for major depressive disorder MRI semantic classification

Abstract

Predicting patients with major depression (MDD) is currently a difficult task. Magnetic resonance imaging (MRI) data analysis may provide insight into individual patient responses, allowing for more customized treatment decisions. Due to the absence of brain MRI data for MDD patients, a transfer learning (TL) method developed is used using calculation criteria. Combining an Inception-v3 neural network with a typical pre-trained neural network, the move learning-based Inception-v3 was proposed for the classification of MDD MRI datasets. An experiment was performed on the classification of eight semantic emotions (defined by IMAPS). Compared to other methods, the proposed method performs high efficiency for 90-10% and 80-20% (positive and negative classes), normal (N), unnormal (UN), and average/total sets, and for 70-30%, accuracy (A) is 92.90%, area under the curve (AUC) is 94.23%, and average precision score (APS) is 95.75%. Individual patients' responses to emotional stimulation can be predicted using the proposed methods, which can provide guidance in diagnosis and prognosis.