Diagnostic of autism spectrum disorder based on structural brain MRI images using, grid search optimization, and convolutional neural networks

In this study, an automatic autism diagnostic model based on sMRI is proposed. This proposed model consists of two basic stages. The first stage is the preprocessing stage, which consists of removing unclear images, identi-fying the edges of the images by applying the canny edge detection (CED) algorithm, cropping them to the size required by the system, and finally enlarging the images five times with data augmentation. The data augmentation method should not affect the discrimination in the images such as coloring, and also since it is applied to both groups of autism spectrum disorders (ASD) and typical development (TD), it is performed with care not to cause any manipulation in the data. In the second stage, the grid search optimization (GSO) algorithm is applied to the deep convolutional neural networks (DCNN) used in the system to have optimal hyper -parameters. As a result, the proposed diagnostic method of ASD based on sMRI achieves an outstanding success rate of 100%. The reliability of the proposed model is validated by testing with five-fold cross-validation, and its superiority is demonstrated by comparing it with recent studies and widely-used pre-trained models.

Automated summary

This study proposes an automatic autism diagnostic model based on sMRI, which achieves outstanding diagnostic accuracy through preprocessing and optimization of neural networks.