自闭症谱系障碍早期发现与严重程度评估的影像与问卷混合方法

IF 4.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Rajkumar S.C. , Stefano Cirillo , Yuvasini D. , Luisa Solimando
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引用次数: 0

摘要

在这项研究中,我们提出了一种新的综合系统,用于自闭症谱系障碍(ASD)婴儿的早期诊断和认知增强。该系统包括两个核心模块:行为分析模块和认知技能增强模块。行为分析模块包括问卷分析子模块和图像分析子模块,前者采用随机森林分类器对问卷数据进行分析,后者采用VGG16卷积神经网络对面部图像进行处理。这些子模块独立评估自闭症谱系障碍的可能性,并结合它们的输出,使用加权平均技术生成全面的诊断。认知技能增强模块集成了互动游戏和基于网络的动画,旨在提高自闭症患儿的认知能力和日常生活技能。此外,奖励系统被纳入强化学习成果,根据婴儿的进步自适应计算奖励。该系统旨在为ASD的诊断和干预提供一个整体的方法,为早期发现和量身定制的认知发展提供一个有效的工具。通过对比分析证明了该系统的有效性,在患有自闭症的幼儿中,诊断准确率提高了93%,认知技能发展提高了92%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A hybrid approach combining images and questionnaires for early detection and severity assessment of Autism Spectrum Disorder
In this research, we propose a novel integrated system for the early diagnosis and cognitive enhancement of infants with Autism Spectrum Disorder (ASD). The system combines two core modules: the Behavioral Analytic Module and the Cognitive Skill Enhancement Module. The Behavioral Analytic Module includes a Questionnaire Analysis Sub-module, which utilizes Random Forest classifiers to analyze questionnaire data, and an Image Analysis Sub-module, which employs a fine-tuned VGG16 Convolutional Neural Network to process facial images. These sub-modules independently assess ASD likelihood and combine their outputs to generate a comprehensive diagnosis using a weighted averaging technique. The Cognitive Skill Enhancement Module integrates interactive games and web-based animations designed to improve cognitive abilities and daily living skills in toddlers with ASD. Additionally, a reward system is incorporated to reinforcement learning outcomes, adaptively calculating rewards based on the infants’ progress. The proposed system aims to provide a holistic approach to ASD diagnosis and intervention, offering an effective tool for early detection and tailored cognitive development. The system’s efficacy is demonstrated through comparative analysis, showing a 93% improvement in diagnostic accuracy and a 92% enhancement in cognitive skill development among toddlers with ASD.
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来源期刊
Image and Vision Computing
Image and Vision Computing 工程技术-工程:电子与电气
CiteScore
8.50
自引率
8.50%
发文量
143
审稿时长
7.8 months
期刊介绍: Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.
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