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English(EN) Screening Autism Spectrum Disorder in children using Deep Learning Approach : Evaluating the classification model of YOLOv26s by comparing with other models

深度学习模型YOLOv26s在通过面部分析筛查自闭症方面显示出潜力

研究人员开发了一种深度学习模型YOLOv26s,利用面部图像筛查儿童的自闭症谱系障碍(ASD)。该模型在区分ASD儿童与典型发育儿童的图像分类任务中达到了92.86%的准确率和0.9291的F1分数。研究表明,像YOLOv26s这样的目标检测模型可以有效地重新用于分类任务,为早期ASD干预提供了一个潜在的工具。 AI

影响 这项研究展示了重新利用的目标检测模型在医疗筛查方面的潜力,可能改善发育障碍的早期诊断。

排序理由 这是一篇研究论文,详细介绍了深度学习模型在特定分类任务中的新颖应用。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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深度学习模型YOLOv26s在通过面部分析筛查自闭症方面显示出潜力

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这是一篇研究论文,详细介绍了深度学习模型在特定分类任务中的新颖应用。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Subash Gautam, Sagar Pathak, Prabin Sharma, Bidhya Shrestha, Kisan Thapa, Shubham Joshi, Mala Deep Upadhaya, Dikshya Thapa, Chandiprasad Chintalapati, Sagar Duwal, Angela Upreti, Salik Ram Khanal ·

    使用深度学习方法筛查儿童自闭症谱系障碍:通过与其他模型比较来评估YOLOv26s的分类模型

    arXiv:2306.14300v2 Announce Type: replace-cross Abstract: Autism spectrum disorder (ASD) is a developmental condition that presents significant challenges in social interac- tion, communication, and behavior. Early intervention plays a pivotal role in enhancing cognitive abilitie…