Convolutional slime mold deep learning model for diagnosis of PD.

IF 1.6 4区医学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-08-20 DOI:10.1080/10255842.2025.2542942

Sk Wasim Akram, A P Siva Kumar

引用次数: 0

Abstract

The proposed study aims to develop an efficient PD detection scheme using a novel optimized deep learning mechanism. Initially, the input multiple human voice recordings are pre-processed to lessen the unwanted noises. Then, the relevant features are selected to reduce the complexity problems in the feature selection stage using chi-square feature statistical model. Finally, an Enhanced Convolutional Slime Mold Attention (ECSMA) model is proposed for categorizing the input voice recordings. The simulation results portray that the proposed PD detection model attains higher performance than other existing methods and mitigate the costs of healthcare in identifying upcoming disease stages.

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用于PD诊断的卷积黏菌深度学习模型。

该研究旨在利用一种新的优化深度学习机制开发一种有效的PD检测方案。首先，输入的多个人的录音经过预处理，以减少不必要的噪音。然后，利用卡方特征统计模型选择相关特征，降低特征选择阶段的复杂性问题。最后，提出了一种增强卷积黏菌注意（ECSMA）模型，用于对输入录音进行分类。仿真结果表明，所提出的PD检测模型比其他现有方法具有更高的性能，并且在识别即将到来的疾病阶段时降低了医疗保健成本。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer Methods in Biomechanics and Biomedical Engineering 工程技术-工程：生物医学

CiteScore

4.10

自引率

6.20%

发文量

179

审稿时长

4-8 weeks

期刊介绍： The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.