Version 1
: Received: 17 July 2024 / Approved: 18 July 2024 / Online: 18 July 2024 (11:28:40 CEST)
How to cite:
Khan, N. A.; Shang, X. ASD-GANNet: A GAN inspired Deep Learning Approach for the Classification of Human’s Autism Brain Disorder. Preprints2024, 2024071466. https://doi.org/10.20944/preprints202407.1466.v1
Khan, N. A.; Shang, X. ASD-GANNet: A GAN inspired Deep Learning Approach for the Classification of Human’s Autism Brain Disorder. Preprints 2024, 2024071466. https://doi.org/10.20944/preprints202407.1466.v1
Khan, N. A.; Shang, X. ASD-GANNet: A GAN inspired Deep Learning Approach for the Classification of Human’s Autism Brain Disorder. Preprints2024, 2024071466. https://doi.org/10.20944/preprints202407.1466.v1
APA Style
Khan, N. A., & Shang, X. (2024). ASD-GANNet: A GAN inspired Deep Learning Approach for the Classification of Human’s Autism Brain Disorder. Preprints. https://doi.org/10.20944/preprints202407.1466.v1
Chicago/Turabian Style
Khan, N. A. and Xuequn Shang. 2024 "ASD-GANNet: A GAN inspired Deep Learning Approach for the Classification of Human’s Autism Brain Disorder" Preprints. https://doi.org/10.20944/preprints202407.1466.v1
Abstract
Classification of pre-processed fMRI dataset using the functional connectivity(FC) based features is considered a challenging task due to high dimensional FC features set and smaller dataset size. To tackle this specific FC high dimensional features set and a smaller dataset size , We have proposed here a GAN based dataset augmenter to firstly train the GAN on the NYU Connectivity Features dataset and used the trained GAN to generate synthetic features per category and after getting sufficient number of features per category , A multi-head attention mechanism was used as a head for the classification . We name our proposed approach as "ASD-GANNet" which is End-To-End and does not require hand-crafted features as the multi-head attention mechanism focuses on the features that are more relevant . Moreover , We compared our results with the 06 available state-of-the-art techniques from the literature and Our proposed approach results using "NYU" site as a training set for generating GAN based synthetic dataset are promising. We achieved an overall 10-fold cross validation based accuracy of 82% ,sensitivity 82% and specificity 81% outperforming the available state-of-the art approaches. Sitewise comparison of our proposed approach also outperformed the the available state-of-the-art as out of the 17 sites our proposed approach has better results in the 10 sites.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
