Deep Learning for Neurodevelopmental Disorder Diagnosis: Leveraging Retinal Images with Self-supervised Learning
DOI:
https://doi.org/10.47611/jsrhs.v13i1.6155Keywords:
Neurodevelopmental Disorder, Self-Supervised Learning, Convolutional Neural NetworkAbstract
Neurodevelopmental disorders like autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) have posed substantial challenges to the cognitive, social, and emotional development of children. Early and precise diagnosis of ASD and ADHD holds significant importance in facilitating timely interventions and support, ultimately enhancing outcomes for individuals affected by these conditions. Conventional diagnostic approaches for these disorders have depended on behavioral assessments, clinical observations, and structured interviews, which are labor-intensive and susceptible to subjective judgments made by healthcare professionals. In this research paper, we proposed a deep learning-based system for diagnosing neurodevelopmental disorders. The proposed system utilizes retinal images as input and generates disorder predictions. We introduce a novel self-supervised learning approach to attain superior accuracy outcomes. We demonstrated that the proposed system achieves state-of-the-art accuracy on large-scale retinal image datasets with extensive experimental results.
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He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). https://doi.org/10.48550/arXiv.1512.03385
Mao, A., Mohri, M., & Zhong, Y. (2023). Cross-entropy loss functions: Theoretical analysis and applications. arXiv preprint arXiv:2304.07288. https://doi.org/10.48550/arXiv.2304.07288
Patton, N., Aslam, T. M., MacGillivray, T., Deary, I. J., Dhillon, B., Eikelboom, R. H., ... & Constable, I. J. (2006). Retinal image analysis: concepts, applications and potential. Progress in retinal and eye research, 25(1), 99-127.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4510-4520). https://doi.org/10.48550/arXiv.1801.04381
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. https://doi.org/10.48550/arXiv.1409.1556
Tan, M., & Le, Q. (2019, May). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning (pp. 6105-6114). PMLR. https://doi.org/10.48550/arXiv.1905.11946
Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., ... & Xiao, B. (2020). Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence, 43(10), 3349-3364. https://doi.org/10.48550/arXiv.1908.07919
Zhou, Y., Chia, M. A., Wagner, S. K., Ayhan, M. S., Williamson, D. J., Struyven, R. R., ... & Keane, P. A. (2023). A foundation model for generalizable disease detection from retinal images. Nature, 1-8.
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Copyright (c) 2024 Beom Kim, Hwan Kim; Sojung Min
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