Title: Autism spectrum disorder detection using convolutional neural network with transfer learning

Authors: Rakhee Kundu; Sunil Kumar

Addresses: Department of Computer Science and Engineering, Amity University Rajasthan, Jaipur, SP-1 Kant Kalwar, NH11C, RIICO Industrial Area, Rajasthan, 303002, India ' Department of Computer Science and Engineering, Amity University Rajasthan, Jaipur, SP-1 Kant Kalwar, NH11C, RIICO Industrial Area, Rajasthan, 303002, India

Abstract: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition, which can vary widely among individuals, making it difficult to establish a uniform set of criteria for diagnosis, leads to underdiagnosis or misdiagnosis in previous researches. This research developed the green anaconda one-to-one-based optimiser-based convolutional neural network with transfer learning (GAOOBO_CNN_TL) for ASD classification utilising multimodal data. Firstly, the image pre-processing process is performed by Kuwahara filters and RoI extraction. Then, the input autism data is normalised by using Z-score. Then, the selection of features is conducted by the Hubert index and data augmentation is done by employing bootstrapping. Then, the ASD classification is done by CNN_TL. Then, weight optimisation is executed using GAOOBO, which is the incorporation of green anaconda optimisation (GAO) and one-to-one-based optimiser (OOBO). The GAOOBO_CNN_TL gained accuracy with 92.823%, specificity with 93.127% and sensitivity with 91.997%.

Keywords: autism spectrum disorder; ASD; green anaconda optimisation; GAO; GoogLeNet; Xception; convolutional neural network; CNN.

DOI: 10.1504/IJAMECHS.2025.147089

International Journal of Advanced Mechatronic Systems, 2025 Vol.12 No.3, pp.147 - 160

Received: 14 Aug 2024
Accepted: 16 Jan 2025
Published online: 10 Jul 2025 *

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