Version 1
: Received: 17 September 2023 / Approved: 18 September 2023 / Online: 18 September 2023 (09:44:09 CEST)
How to cite:
Gogoi, D.K.; Talukdar, J.; Bhattacharyya, D.K.; Singh, T.P. A Deep Learning Approach to Classify Autism Spectrum Disorder using MRI Images. Preprints2023, 2023091157. https://doi.org/10.20944/preprints202309.1157.v1
Gogoi, D.K.; Talukdar, J.; Bhattacharyya, D.K.; Singh, T.P. A Deep Learning Approach to Classify Autism Spectrum Disorder using MRI Images. Preprints 2023, 2023091157. https://doi.org/10.20944/preprints202309.1157.v1
Gogoi, D.K.; Talukdar, J.; Bhattacharyya, D.K.; Singh, T.P. A Deep Learning Approach to Classify Autism Spectrum Disorder using MRI Images. Preprints2023, 2023091157. https://doi.org/10.20944/preprints202309.1157.v1
APA Style
Gogoi, D.K., Talukdar, J., Bhattacharyya, D.K., & Singh, T.P. (2023). A Deep Learning Approach to Classify Autism Spectrum Disorder using MRI Images. Preprints. https://doi.org/10.20944/preprints202309.1157.v1
Chicago/Turabian Style
Gogoi, D.K., Dhruba Kumar Bhattacharyya and Thipendra P Singh. 2023 "A Deep Learning Approach to Classify Autism Spectrum Disorder using MRI Images" Preprints. https://doi.org/10.20944/preprints202309.1157.v1
Abstract
Autism Spectrum Disorder (ASD) is a neuro developmental condition that impacts social interac-tion, communication, and behavior. It is called a spectrum because its symptoms and degrees of severity can vary greatly from one person to the next. Differentiations in brain structure and function have been observed between autistic and typically developing brains. Various aspects of brain function, including information processing, sensory perception, and the capacity to interpret and respond to social cues, may be affected by these differences. The signs and symptoms of ASD are readily apparent to parents in the first few years of their child’s life. The symptoms of ASD, it is said, first appear in early childhood and continue into maturity. Brain MRI analysis utilizing deep learning methods show encouraging findings in the study of ASD. In this study, we look at how well a customized VGG16 architecture compares models that have already been trained. Inception v3, ResNet50, DenseNet121, and MobileNet are used to identify and analyze ASD problems in a brain MRI dataset from people with ASD. The proposed methods have been vali-dated on the unlabeled ABIDE 1 dataset that is freely available to the public. After the unlabeled data has been clustered, five different Deep Learning (DL) methods are used and compared. The results are compared to those of other studies that have already been conducted.
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.
