Novel deep learning models with novel integrated activation functions for autism screening: AutiNet and MinAutiNet

Abstract

Deep Learning (DL) particularly CNN has the potential to outplay conventional Machine Learning (ML) procedures in the realm of computer vision. With the advent of DL, profound research on autism utilizing fMRI data has declared advancements in autism screening. Yet, DL models are inadequate for heterogeneous datasets encompassing complex fMRI data. To resolve these problems, two cutting-edge AutiNet and MinAutiNet models are proposed with a minimum number of layers and neurons in classifying the autistic subjects. Both AutiNet and MinAutiNet models are the two refined forms of the traditional LeNet-5 model. AutiNet model includes two convolutional layers, two average pooling layers, a flatten layer, and one fully connected layer. One convolutional layer, one average pooling layer, one flatten layer, and one fully connected layer make up the MinAutiNet model. Pre-processing is a pre-requisite phase in the AutiNet and MinAutiNet models where denoising of raw fMRI data is done with slice time correction, realignment, min–max normalization and one hot encoding to obtain numerical values. As inspired by the promising results of non-handcrafted feature extraction, the proposed DL models have been used for feature extraction. Two novel integrated activation functions namely Li-ReLU and S-RReLU are developed and incorporated into the proposed DL models towards improving the performance of autism screening. Both AutiNet and MinAutiNet models surpass the standard accuracy in classifying autistic subjects with minimum number of layers and neurons. Experimental results reveal that the proposed AutiNet attains the maximum accuracy of 77.78% with Caltech and KKI datasets and the proposed MinAutiNet achieves the maximum accuracy of 88.89% with SBL dataset. The proposed models are compared with other state-of-the-art works to ensure its superiority.