فرناز درويشي

Depression is one of the main causes of sadness and inability of people to do the simplest daily tasks in the world and it has increased by about 50% during the last three decades. Depression and anxiety can lead to serious health crises and even suicide attempts. Although various treatments are available for depression, such facilities are not available in underdeveloped countries and even developing countries which are not so advanced. Therefore, to treat depression in these countries, first of all, it is very necessary to diagnose depression in the shortest possible time otherwise starting the necessary treatments and dealing with the disease will be very difficult and long for the patient. In this research, a comparative study focusing on existing depression diagnosis models is presented to examine the strengths and weaknesses of each and it was concluded that the methods based on deep learning have better performance. To implement the proposed research model, the image data is first pre-processed before entering the network, which includes things such as changing the size of the input images and labeling in this first research, a comparative study focusing on existing depression diagnosis models is presented to examine the strengths and weaknesses of each and it was concluded that the methods based on deep learning have better performance. To implement the proposed research model, the image data is pre-processed before entering the network, which includes things such as resizing the input images and labeling then the additional information of each image, such as the information related to the filter and the name of each channel, is removed and only the signal values are kept, and the information of each data is stored in small periods including 100 seconds of signals(batch size is 100 s) , and the call is made in periods smaller than that i.e. batch size 80 seconds which In a way, it is considered a data augmentation process that improves network learning and finally the implementation of convolution neural network and deep learning techniques is performed on the pre-processed and trained data set. In this respect, this study simultaneously eva‎luates the accuracy of existing models and presents its proposed model. In this research, the dataset prepared from the Figshare site has been used. Implementation and comparison of deep learning models in three neural networks VGG16, residual neural network and Inception neural network and each of them reached the test accuracy of 0.962, 0.846, 0.575 respectively, which shows that VGG16 neural network is more accurate than other networks. The reference article uses the data set of Lanzhou University and the convolution neural network method achieves an accuracy of 0.943, which is less accurate than this research.

تشخيص افسردگي، شبكه ي عصبي پيچشي، يادگيري عميق

depression diagnosis, convolution neural network, deep learning

farnaz darvishi

aliasghar beheshti shirazi