深度学习模型在寄生虫感染诊断中的比较评价。

IF 3 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Omid Mirzaei, Ahmet Ilhan, Emrah Guler, Kaya Suer, Boran Sekeroglu
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引用次数: 0

摘要

(1)背景:蠕虫感染是一个广泛的全球健康问题,蛔虫和绦虫病是最常见的两种感染。传统的诊断方法,如基于卵子的显微镜,充满了挑战,包括主观性和低通量,经常导致误诊。本研究评估了先进的深度学习模型在从显微镜图像中准确分类蛔虫和带绦虫卵方面的功效,提出了一种技术增强的临床诊断方法。(2)方法:考虑了三种最先进的深度学习模型,ConvNeXt Tiny, EfficientNet V2 S和MobileNet V3 S。通过进行多类别实验,利用包括蛔虫、带绦虫和未感染卵图像的多样化数据集来训练和验证这些模型。(3)结果:所有模型均表现出较高的分类准确率,其中ConvNeXt Tiny的f1得分为98.6%,其次是EfficientNet V2 S的97.5%和MobileNet V3 S的98.2%。这些结果证明了深度学习在简化和改进蠕虫感染诊断过程中的潜力。深度学习模型(如ConvNeXt Tiny、EfficientNet V2 S和MobileNet V3 S)的应用有望实现高效、准确的蠕虫卵分类,有可能显著增强诊断工作流程。(4)结论:该研究证明了利用先进的计算技术在寄生虫学中的可行性,并指出了快速、客观和可靠的诊断标准的未来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative Evaluation of Deep Learning Models for Diagnosis of Helminth Infections.

(1) Background: Helminth infections are a widespread global health concern, with Ascaris and taeniasis representing two of the most prevalent infestations. Traditional diagnostic methods, such as egg-based microscopy, are fraught with challenges, including subjectivity and low throughput, often leading to misdiagnosis. This study evaluates the efficacy of advanced deep learning models in accurately classifying Ascaris lumbricoides and Taenia saginata eggs from microscopic images, proposing a technologically enhanced approach for diagnostics in clinical settings. (2) Methods: Three state-of-the-art deep learning models, ConvNeXt Tiny, EfficientNet V2 S, and MobileNet V3 S, are considered. A diverse dataset comprising images of Ascaris, Taenia, and uninfected eggs was utilized for training and validating these models by performing multiclass experiments. (3) Results: All models demonstrated high classificatory accuracy, with ConvNeXt Tiny achieving an F1-score of 98.6%, followed by EfficientNet V2 S at 97.5% and MobileNet V3 S at 98.2% in the experiments. These results prove the potential of deep learning in streamlining and improving the diagnostic process for helminthic infections. The application of deep learning models such as ConvNeXt Tiny, EfficientNet V2 S, and MobileNet V3 S shows promise for efficient and accurate helminth egg classification, potentially significantly enhancing the diagnostic workflow. (4) Conclusion: The study demonstrates the feasibility of leveraging advanced computational techniques in parasitology and points towards a future where rapid, objective, and reliable diagnostics are standard.

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来源期刊
Journal of Personalized Medicine
Journal of Personalized Medicine Medicine-Medicine (miscellaneous)
CiteScore
4.10
自引率
0.00%
发文量
1878
审稿时长
11 weeks
期刊介绍: Journal of Personalized Medicine (JPM; ISSN 2075-4426) is an international, open access journal aimed at bringing all aspects of personalized medicine to one platform. JPM publishes cutting edge, innovative preclinical and translational scientific research and technologies related to personalized medicine (e.g., pharmacogenomics/proteomics, systems biology). JPM recognizes that personalized medicine—the assessment of genetic, environmental and host factors that cause variability of individuals—is a challenging, transdisciplinary topic that requires discussions from a range of experts. For a comprehensive perspective of personalized medicine, JPM aims to integrate expertise from the molecular and translational sciences, therapeutics and diagnostics, as well as discussions of regulatory, social, ethical and policy aspects. We provide a forum to bring together academic and clinical researchers, biotechnology, diagnostic and pharmaceutical companies, health professionals, regulatory and ethical experts, and government and regulatory authorities.
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