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BRIEF RESEARCH REPORT article
Front. Psychiatry
Sec. Computational Psychiatry
Volume 15 - 2024 |
doi: 10.3389/fpsyt.2024.1395243
This article is part of the Research Topic Deep Learning for High-Dimensional Sense, Non-Linear Signal Processing and Intelligent Diagnosis View all 4 articles
Generation and Discrimination of Autism MRI Images Based on Autoencoder
Provisionally accepted- 1 Hubei Polytechnic University, Huangshi, China
- 2 Huanggang Normal University, Huanggang, Hubei Province, China
This study aims to explore an autoencoder-based method for generating brain MRI images of patients with Autism Spectrum Disorder (ASD) and non-ASD individuals, and to discriminate ASD based on the generated images. Initially, we introduce the research background of ASD and related work, as well as the application of deep learning in the field of medical imaging. Subsequently, we detail the architecture and training process of the proposed autoencoder model, and present the results of generating MRI images for ASD and non-ASD patients. Following this, we designed an ASD classifier based on the generated images and elucidated its structure and training methods. Finally, through analysis and discussion of experimental results, we validated the effectiveness of the proposed method and explored future research directions and potential clinical applications. This research offers new insights and methodologies for addressing challenges in ASD studies using deep learning technology, potentially contributing to the automated diagnosis and research of ASD.
Keywords: deep autoencoder, sMRI, Image generation, image classification, ASD
Received: 03 Mar 2024; Accepted: 16 Sep 2024.
Copyright: © 2024 Shi, Gong, Guan and Tang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yongli Gong, Hubei Polytechnic University, Huangshi, China
Yurong Guan, Huanggang Normal University, Huanggang, 438000, Hubei Province, China
Jiawei Tang, Hubei Polytechnic University, Huangshi, China
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