Depression Detection Using Feature Extraction and Deep Learning from sMRI Images

Major Depression Disorder (MDD) affects people's life and it is a common disorder worldwide. Finding useful diagnostic biomarkers would help clinicians to diagnosis MDD in its early stages. Machine learning algorithms for brain imaging classification of MDD is beneficial although it is challenging. In this paper, we investigate utilizing augmentation, feature extraction and classification for MDD detection from sMRI Images. Using images, we extract 10 slices with highest entropy values from each 3D sMRI image as raw features. The selected slices are the most representative 2D slices of each volume and the problem is reduced to classifying these slices. Next, augmentation is done by either duplication or rotation to balance the data. Then deep learning algorithms such as convolutional neural network and pre-trained networks with or without fine tuning are applied to extract features automatically. We compared several feature extraction methods in combination with SVM classifiers with linear or RBF kernels. Experiments show that raw features work pretty well for MDD classification. Also, pre-trained VGG16 with fine-tuning produces good results. However, pre-trained network without fine tuning returns acceptable result with short training time.