سامانه همایش های دانشگاه آزاد اسلامی واحد نجف آباد

صفحه اصلی / International Conference on Artificial Intelligence; City, Industry and Health

Diagnosis of Autism in Brain MRI Images Using Fuzzy Clustering and Gaussian Mixture, Feature Extraction with VGG-16, and Classification with ResNet

نویسندگان :

Mansoor Zeinali¹ Ghadeer ketab yousif Alkhafaji²

1- Nahafabad Branch, Islamic Azad University 2- Nahafabad Branch, Islamic Azad University

کلمات کلیدی :

Autism،ResNet،MRI،VGG-16

چکیده :

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that necessitates timely and accurate diagnosis for effective intervention. This thesis presents a hybrid deep learning model for detecting ASD, employing advanced preprocessing, multi-channel images, and an optimized ResNet classifier enhanced by a short-horn optimization algorithm. This method significantly improves the accuracy and reliability of autism diagnosis, achieving 99.48% accuracy, 98.96% precision, 100% recall, and an F1-Score of 99.48% when evaluated on the ABIDE I dataset. By integrating clustering techniques such as Fuzzy C-Means (FCM) and Gaussian Mixture Models (GMM), the model minimizes manual feature engineering, effectively handling high-dimensional MRI data and offering a scalable, efficient solution for autism diagnosis.

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