TY - GEN
T1 - Classification of Autism Spectrum Disorder Based on Brain Image Data Using Deep Neural Networks
AU - Lakshmi, Polavarapu Bhagya
AU - Reddy, V. Dinesh
AU - Ghosh, Shantanu
AU - Sengar, Sandeep Singh
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023/11/21
Y1 - 2023/11/21
N2 - Autism spectrum disorder (ASD) is a neuro-developmental disorder that affects 1% of children and has a lifetime effect on communication and interaction. Early prediction can address this problem by decreasing the severity. This paper presents a deep learning-based transfer learning applied to resting state fMRI images for predicting the autism disorder features. We worked with CNN and different transfer learning models such as Inception-V3, Resnet, Densenet, VGG16, and Mobilenet. We performed extensive experiments and provided a comparative study for different transfer learning models to predict the classification of ASD. Results demonstrated that VGG16 achieves high classification accuracy of 95.8% and outperforms the rest of the transfer learning models proposed in this paper and has an average improvement of 4.96% in terms of accuracy.
AB - Autism spectrum disorder (ASD) is a neuro-developmental disorder that affects 1% of children and has a lifetime effect on communication and interaction. Early prediction can address this problem by decreasing the severity. This paper presents a deep learning-based transfer learning applied to resting state fMRI images for predicting the autism disorder features. We worked with CNN and different transfer learning models such as Inception-V3, Resnet, Densenet, VGG16, and Mobilenet. We performed extensive experiments and provided a comparative study for different transfer learning models to predict the classification of ASD. Results demonstrated that VGG16 achieves high classification accuracy of 95.8% and outperforms the rest of the transfer learning models proposed in this paper and has an average improvement of 4.96% in terms of accuracy.
KW - Autism
KW - Densenet
KW - Inception-V3
KW - Mobilenet
KW - Resnet
KW - VGG16
UR - https://www.scopus.com/pages/publications/85177879155
U2 - 10.1007/978-981-99-6702-5_17
DO - 10.1007/978-981-99-6702-5_17
M3 - Conference contribution
AN - SCOPUS:85177879155
SN - 9789819967018
T3 - Smart Innovation, Systems and Technologies
SP - 209
EP - 218
BT - Evolution in Computational Intelligence - Proceedings of the 11th International Conference on Frontiers of Intelligent Computing
A2 - Bhateja, Vikrant
A2 - Yang, Xin-She
A2 - Ferreira, Marta Campos
A2 - Sengar, Sandeep Singh
A2 - Travieso-Gonzalez, Carlos M.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023
Y2 - 11 April 2023 through 12 April 2023
ER -