Kittichai Veerayuth, Sompong Weerachat, Kaewthamasorn Morakot, Sasisaowapak Thanyathep, Naing Kaung Myat, Tongloy Teerawat, Chuwongin Santhad, Thanee Suchansa, Boonsang Siridech
Faculty of Medicine, King Mongkut's Institute of Technology Ladkrabang, Thailand.
Veterinary Parasitology Research Unit, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, Thailand.
Heliyon. 2024 May 5;10(9):e30643. doi: 10.1016/j.heliyon.2024.e30643. eCollection 2024 May 15.
Trypanosomiasis, a significant health concern in South America, South Asia, and Southeast Asia, requires active surveys to effectively control the disease. To address this, we have developed a hybrid model that combines deep metric learning (DML) and image retrieval. This model is proficient at identifying Trypanosoma species in microscopic images of thin-blood film examinations. Utilizing the ResNet50 backbone neural network, a trained-model has demonstrated outstanding performance, achieving an accuracy exceeding 99.71 % and up to 96 % in recall. Acknowledging the necessity for automated tools in field scenarios, we demonstrated the potential of our model as an autonomous screening approach. This was achieved by using prevailing convolutional neural network (CNN) applications, and vector database based-images returned by the KNN algorithm. This achievement is primarily attributed to the implementation of the Triplet Margin Loss function as 98 % of precision. The robustness of the model demonstrated in five-fold cross-validation highlights the ResNet50 neural network, based on DML, as a state-of-the-art CNN model as AUC >98 %. The adoption of DML significantly improves the performance of the model, remaining unaffected by variations in the dataset and rendering it a useful tool for fieldwork studies. DML offers several advantages over conventional classification model to manage large-scale datasets with a high volume of classes, enhancing scalability. The model has the capacity to generalize to novel classes that were not encountered during training, proving particularly advantageous in scenarios where new classes may consistently emerge. It is also well suited for applications requiring precise recognition, especially in discriminating between closely related classes. Furthermore, the DML exhibits greater resilience to issues related to class imbalance, as it concentrates on learning distances or similarities, which are more tolerant to such imbalances. These contributions significantly make the effectiveness and practicality of DML model, particularly in in fieldwork research.
锥虫病是南美洲、南亚和东南亚地区一个重大的健康问题，需要进行积极的调查以有效控制该疾病。为解决这一问题，我们开发了一种结合深度度量学习（DML）和图像检索的混合模型。该模型擅长在薄血膜检查的显微图像中识别锥虫种类。利用ResNet50骨干神经网络，一个经过训练的模型表现出色，准确率超过99.71%，召回率高达96%。认识到现场场景中对自动化工具的需求，我们展示了该模型作为自主筛查方法的潜力。这是通过使用流行的卷积神经网络（CNN）应用以及KNN算法返回的基于向量数据库的图像实现的。这一成果主要归功于三元组边缘损失函数的实施，精度达到98%。在五折交叉验证中展示的模型稳健性突出了基于DML的ResNet50神经网络作为AUC>98%的先进CNN模型。DML的采用显著提高了模型的性能，不受数据集变化的影响，使其成为实地研究的有用工具。与传统分类模型相比，DML在管理具有大量类别的大规模数据集方面具有几个优势，增强了可扩展性。该模型有能力推广到训练期间未遇到的新类别，在可能不断出现新类别的场景中证明特别有利。它也非常适合需要精确识别的应用，特别是在区分密切相关的类别方面。此外，DML对与类不平衡相关的问题表现出更大的弹性，因为它专注于学习距离或相似性，对这种不平衡更具容忍性。这些贡献显著提高了DML模型的有效性和实用性，特别是在实地研究中。
