RBM-GP with novel kernels coupled deep learning model for autism screening

Abstract

Autism Spectrum Disorder (ASD) assumes greater significance because of its worldwide prevalence and the need to detect it in its preliminary stage is imperative. No standard medical test exists universally to screen autism. Each and every aspect of our life proves that nature holds the reign. This could be well demonstrated through many prevailing research works which still fails to classify ASD individuals accurately. In the proposed work, a unique framework, RBM-GP (Restricted Boltzmann Machine-Gaussian Process) along with two novel kernels namely RPR and RQ-P are proposed to address the need for automatic classification of ASD individuals and Typical Controls (TC) thereupon with minimum error. Slice Time Correction and Head Motion Correction are carried out as a preparatory step to take care of noisy fMRI (functional Magnetic Resonance Imaging) data. To make the best use of high dimensional fMRI data, the pre-processed fMRI data is normalized for further processing. In the proposed system, deep learning model is executed to extract non-handcrafted features from the pre-processed fMRI data with the notion of attaining less error during classification process. As a result, Bernoulli RBM acts as a feature extractor to derive highly discriminative fMRI features. In order to develop an effective computerized system for the classification of autistic subjects, GP regression, a machine learning classifier is employed in the proposed system to learn deep fMRI features. Kernel plays a vital role in the GP regression. So, RPR and RQ-P kernels are developed and integrated with GP in the proposed work for the accurate classification of autistic subjects. All datasets from the ABIDE I database are utilized for conducting the experiments. The proposed system achieves the minimum Mean Squared Error (MSE) of 20% for USM dataset with RPR kernel and CMU_b dataset with RQ-P kernel. The experimental results and comprehensive result analysis proclaim the superiority of the proposed system than the existing state-of-the-art approaches.