Stacked autoencoder with novel integrated activation functions for the diagnosis of autism spectrum disorder - M, Kaviya Elakkiya; Dejey (Neural computing & applications, 2023)
Autism screening is crucial for the early diagnosis of developmental disorder. The combination of machine learning (ML) and deep learning (DL) approaches are applied to produce memory efficient and less complex deep learning models for the computer aided diagnosis (CAD) of autism screening. In the proposed work, two novel integrated activation functions such as Li-ReLU and S-RReLU are developed to aid in the classification of autistic subjects and typical controls (TC) with maximum accuracy. As functional magnetic resonance imaging (fMRI) data is noisy, it undergoes temporal and spatial pre-processing. The artifact free high dimensional fMRI data is exercised for the process of feature extraction and dimensionality reduction employing group principal component analysis (Group PCA) and group independent component analysis (Group ICA). The selected features are normalized using 0–1 normalization and converted to tensors. Stacked autoencoder (SAE) utilizes the fMRI tensor data for the classification of autism spectrum disorder (ASD) subjects and typical controls. The proposed work is implemented and tested on all datasets of ABIDE I database. The validation accuracy of CMU_a, KKI, UCLA_2, OLIN, Yale and NYU datasets are obtained as 100, 80, 71.43, 100, 85.71 and 93.33% using novel Li-ReLU activation function in the proposed system. With the help of new activation function called S-RReLU, the proposed system achieves validation accuracy of about 10, 100, 57.14, 100, 78.57 and 93.33% for CMU_a, KKI, UCLA_2, OLIN, Yale and NYU datasets. Thus, the proposed method outperforms all other existing state-of-the-art works in terms of accuracy.