Abstract

Autism Spectrum Disorder (ASD) represents a complicated neurodevelopmental condition where early diagnosis is imperative for effective intervention. Behavior assessments, which are somewhat the norm, primarily slow down diagnosis. This thesis presents a hybrid deep learning approach using brain MRI data for the early detection of autism. This approach combines FCM and GMM clustering MRI data, the multi-channel deep learning ResNet architecture for image classification, and the Dwarf Mongoose Optimization Algorithm for feature enhancement, model refinement, and improving generalization. The proposed model achieved an accuracy of 93.72%, precision of 94.81%, recall of 93.14%, and an F1-score of 93.97% when evaluated on the ABIDE I dataset. These results demonstrate and give evidence of the model overcoming challenges related to previously insufficient models in overfitting and predictive performance. This research is geared towards helping automate feature extraction on highly dimensional MRI data having a great scope for ASD clinical diagnosis. This can benefit ASD clinical diagnosis and serve as a starting point for research in advanced ASD clinical diagnosis.