Adaptive Deep Convolution Neural Network for Early Diagnosis of Autism through Combining Personal Characteristic with Eye Tracking Path Imaging - Palanichamy, Naveen; Haw, Su-Cheng; Ng, Kok-Why; Kesavan, Revathi (Society of Visual Informatics, 2025)
Autism is a large set of illnesses related to brain development, also referred to as autism spectrum disorder (ASD). According to WHO reports, 1 in 100 children is expected to have ASD. Numerous behavioral domains are affected, including linguistic, interpersonal skills, stereotypical and repetitive behaviors which represent an extreme instance of a neurodevelopmental abnormality. Identifying ASD can be difficult and exhausting because its symptoms are remarkably identical to those of many other disorders of the mind. Medical professionals can improve diagnosis efficiency by adapting deep learning practices. In clinics for autism spectrum disorders, eye-tracking scan pathways (ETSP) have become a more common instrument. This approach uses quantitative eye movement analysis to study attentional processes, and it exhibits promising results in the development of indicators that can be used in clinical studies for autism. ASD can be identified by comparing the abnormal attention span patterns of children’s having the disorder to the children’s who are typically developing. The recommended model makes use of two publicly viable datasets, namely ABIDE and ETSP imaging. The proposed deep convolutional network consists of four hidden convolution layers and uses 5-fold cross-validation strategy. The performance of the proposed model is validated against multilayer perceptron (MLP) and conventional machine learning classifiers like decision tree (DT), k-nearest neighbor (KNN) and Random Forest (RF) using metrics like sensitivity, specificity and area under curve (AUC). The findings demonstrated that without the need for human assistance, the suggested model is capable of correctly identifying children with ASD.