Early Detection of Autism Spectrum Disorder using Transfer Learning on Brain Imaging Data

This study focuses on classifying and representing learning tasks using powerful deep learning models, including Convolutional Neural Networks (CNN) and Transfer Learning algorithms. The analysis utilizes data from the Autism Brain Imaging Data Exchange (ABIDE I and ABIDE II) datasets. It explores the application of deep learning techniques to enhance the detection of Autism Spectrum Disorder (ASD). Functional magnetic resonance imaging (fMRI) data has shown potential in identifying brain irregularities associated with ASD. We utilize Convolutional Neural Networks (CNNs) combined with transfer learning to analyse fMRI data from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Our optimized CNN model achieves an accuracy of 81%, surpassing conventional classification models. This research establishes deep learning as a promising tool for ASD diagnosis, evaluated based on accuracy, precision, and recall