Optimized Deep Convolutional Neural Network for Autism Spectrum Disorder Detection Using Structural MRI and DTPSO

Detection of Autism Spectrum Disorders (ASD) remains challenging due to complex psychiatric symptoms and limited neurobiological evidence. To address this, an automatic model is developed in this study for detecting ASD and Typically Developing (TD) individuals. The Autism Brain Imaging Data Exchange (ABIDE) dataset, which includes structural Magnetic Resonance Imaging (sMRI), is used in this study for model evaluation. The proposed methodology consists of two main stages: preprocessing and model optimization. In the preprocessing stage, unclear images are discarded, and edges are detected using the Canny Deriche Edge Detection (CDED) algorithm. Subsequently, images are resized, and data augmentation techniques are applied. The Deep Convolutional Neural Networks (DCNN) are fine-tuned using the Dipper-Throated Particle Swarm Optimization (DTPSO) algorithm. Additionally, Class Activation Mapping (Grad-CAM) is utilized to visualize and interpret the model's decision-making process. The proposed model has shown the highest performance with a 95.9% accuracy, 96.5% precision, 95.9% sensitivity, 95.9% specificity, a 96.2% F1-score, and a 94.5% AUC. These results validate the effectiveness of the proposed CDED-DCNN-DTPSO approach in detecting ASD compared to the DCNN-DTPSO model. The proposed model demonstrates higher accuracy, precision, sensitivity, specificity, F1-score, and AUC, indicating its performance in identifying ASD.