Mirzaei Omid, Ilhan Ahmet, Guler Emrah, Suer Kaya, Sekeroglu Boran
Department of Biomedical Engineering, Faculty of Engineering, Near East University, Nicosia 99138, TRNC, Mersin 10, Turkey.
Department of Computer Engineering, Faculty of Engineering, Near East University, Nicosia 99138, TRNC, Mersin 10, Turkey.
J Pers Med. 2025 Mar 20;15(3):121. doi: 10.3390/jpm15030121.
(1) : 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 and eggs from microscopic images, proposing a technologically enhanced approach for diagnostics in clinical settings. (2) : 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) : 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) : 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.
（1）：蠕虫感染是一个广泛存在的全球健康问题，蛔虫和绦虫感染是最常见的两种感染类型。传统的诊断方法，如基于虫卵的显微镜检查，存在诸多挑战，包括主观性和低通量，常常导致误诊。本研究评估了先进的深度学习模型在从显微图像中准确分类蛔虫和绦虫卵方面的有效性，提出了一种在临床环境中技术增强的诊断方法。（2）：考虑了三种先进的深度学习模型，即ConvNeXt Tiny、EfficientNet V2 S和MobileNet V3 S。通过进行多类实验，利用一个包含蛔虫、绦虫和未感染虫卵图像的多样化数据集对这些模型进行训练和验证。（3）：所有模型都表现出了较高的分类准确率，在实验中，ConvNeXt Tiny的F1分数达到了98.6%，其次是EfficientNet V2 S，为97.5%，MobileNet V3 S为98.2%。这些结果证明了深度学习在简化和改善蠕虫感染诊断过程方面的潜力。应用ConvNeXt Tiny、EfficientNet V2 S和MobileNet V3 S等深度学习模型在高效准确的蠕虫卵分类方面显示出了前景，有可能显著改善诊断工作流程。（4）：该研究证明了在寄生虫学中利用先进计算技术的可行性，并指向一个快速、客观和可靠的诊断成为标准的未来。
