Abstract

Autism spectrum disorder (ASD) encompasses a wide range of complex neurodevelopment conditions, posing significant challenges to early diagnosis. In fact, other than early diagnosis (i.e., before normal development) it is extremely challenging to adequately classify these subjects as there are no behavioral markers that are unique to ASD vs. non-ASD subjects. But the early detection is very important in helping with some of the developmental trajectories as the timely intervention which they are getting and the access which the kids and families have to specially designed therapies and support systems will be better. Neuroimaging in autism: a comprehensive review from a machine learning perspective. Autism Research- We investigate the effectiveness of CNN models and a hybrid CNN-RNN architecture in classifying ASD based on the neuroimaging data with an aim of improving feature extraction from the neuroimaging data using the Automated Anatomical Labeling (AAL) map. A key challenge in this task is how to build representational feature maps that can elucidate the intricate patterns across brain regions linked with ASD. Various kinds of models are tested VGG16 2D CNN, VGG16 3D CNN, EfficientNetV2, Inception v3, ResNet50 2D CNN and Hybrid model (CNN-RNN). While going for traditional CNN models gave at least 60% to 69% accuracies, the accuracy from hybrid CNN-RNN model, which combines spatial and temporal features, far outperformed other models with an accuracy of 100%. Thus, hybrid deep learning architectures have a substantial role in the classification of ASD. An involvement of AAL mapping and deep learning methods in this study proposed that the semi-automated approach could be implemented in forensic precision for the early diagnosis of ASD. These findings guide future neuroimaging-based diagnostic research and support the development of more sophisticated hybrid models for classifying autism spectrum disorder.

Authors

Sanju S. Anand¹, Shashidhar Kini²
Srinivas University, India¹, Srinivas Institute of Technology, India²

Keywords

Autism Spectrum Disorder (ASD), Deep Learning, Convolutional Neural Networks (CNN), Hybrid CNN-RNN Models, AAL Map (Automated Anatomical Labeling), Neuroimaging, ASD Classification, Early Detection, and Feature Extraction