Deep Learning Framework using Siamese Neural Network for Diagnosis of Autism from Brain Magnetic Resonance Imaging

Autism spectrum disorder (ASD) is characterized by structural and functional brain changes that contribute to memory, attention and social interaction. The aim of this research is to develop a deep learning framework using Siamese neural nets for computer aided diagnosis of ASD using T1-weighted magnetic resonance imaging (MRI) of 102 control and 112 ASD patients from autism brain imaging data exchange. The preprocessing of the images involves reorientation to a standard space, cropping followed by affine registration to a template. Siamese Neural Network (SNN) with pre-trained ResNet50 model was employed for this study. After preprocessing, the affine registered images are down sampled and reshaped to match with the required input size of the ResNet50. Further, 1070 positive and negative image pairs are formed for training and validation of the SNN model. Final layer of ResNet50 is global averaged and an extra dense layer is added which represents the input image embedding. Further, L1-distance is computed between the embeddings of the two inputs which is further used to backpropagate the error computed using the contrastive loss function. The quality metrics used during 5-fold stratified cross-validation are accuracy, recall, precision and f1-score and these metrics reached a value of 0.99 during validation. Therefore, the developed SNN based tool could be used for diagnosis of autism from T1-weighted MRI.