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Intelligent identification of medical and veterinary intracellular protozoa by using self-supervised learning
(2026-12-01)
Kittichai, Veerayuth
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Kaewthamasorn, Morakot
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Soiphet, Naruchit
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Tongloy, Teerawat
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Chuwongin, Santhad
Background: Zoonotic diseases pose a major threat to both human and animal health, contributing significantly to global morbidity and mortality. Accurate diagnosis is crucial for effective control and treatment, with microscopic examination serving as the gold standard, supplemented by highly sensitive molecular biology techniques. However, these confirmatory methods require skilled personnel and are subject to inter- and intra-rater variability. An innovative solution lies in artificial intelligence (AI)-powered automated tools, which offer a promising alternative. This study aimed to develop a self-supervised learning (SSL) approach using the Distillation with No Labels (DiNOv2) algorithm to extract features of protozoa from Giemsa-stained blood samples. Methods: The development of self-supervised learning algorithms, including DiNOv2, was based on archived samples of clinically significant and significant veterinary microorganisms. These models were evaluated in comparison to a baseline Vision Transformer (ViT). Results: Among the tested SSL models, the DiNOv2-Small version achieved exceptional performance, surpassing 99% accuracy and specificity while maintaining the lowest misclassification rate (0.263). It also demonstrated a high area under the curve (AUC) value of 0.990, underscoring its robust classification capability. Remarkably, even when trained on only 20% of the dataset, the SSL models retained performance levels comparable to baseline models trained on the full dataset. However, further reducing the sample size below 20% led to notable declines in evaluation metrics: accuracy dropped by 5–7%, recall decreased from 37.9% to 35.1%, precision fell from 35% to 25.2%, and the F1 score declined from 31.1% to 21.5%. Additionally, the AUC decreased by 4–11%, while the misclassification rate increased, indicating reduced robustness. A key limitation of this study was the highly imbalanced fine-tuned dataset between classes. Nevertheless, the inconsistent performance observed may be mitigated by employing the larger DiNOv2 model, which improves the F1 score and enhances the model’s ability to handle imbalanced data while reducing reliance on labeled data. Conclusions: The proposed method can assist laboratory technicians, particularly in resource-limited healthcare settings. Furthermore, the findings support the potential deployment of this AI-based tool for automated screening in both medical and veterinary applications.
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Performance validation of deep-learning-based approach in stool examination
(2025-12-01)
Corpuz, Kristal Dale Felimon
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Kusolsuk, Teera
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Wongphan, Benjamaporn
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Chonsawat, Putza
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Naing, Kaung Myat
Background: Human intestinal parasitic infections (IPI) pose a significant global health issue caused by parasitic helminths and protozoa, affecting around 3.5 billion people worldwide, with more than 200,000 deaths annually. Despite advancements in molecular methods with higher sensitivity and specificity, the Kato-Katz or formalin-ethyl acetate centrifugation technique (FECT) remains the gold standard and a routine diagnostic procedure suitable for its simplicity and cost-effectiveness. However, these techniques have limitations that must be addressed. Thus, this study evaluated the performance of a deep-learning-based approach for intestinal parasite identification and compared it with that of human experts. Methods: Human experts performed FECT and Merthiolate-iodine-formalin (MIF) techniques to serve as ground truth and reference for parasite species. Subsequently, a modified direct smear was conducted to gather images for the training (80%) and testing (20%) datasets. State-of-the-art models, including YOLOv4-tiny, YOLOv7-tiny, YOLOv8-m, ResNet-50, and DINOv2 (base, small, and large), were employed and were operated using in-house CIRA CORE platform. Overall performance was evaluated using confusion matrices, the metrics of which were calculated on the basis of the one-versus-rest and micro-averaging approaches. Moreover, the receiver operating characteristic (ROC) and precision-recall (PR) curves were determined for visual comparison. Lastly, Cohen’s Kappa and Bland–Altman analyses were used to statistically measure the significant differences and visualize the association levels between the human experts and the deep learning models’ classification performance in intestinal parasite identification. Results: Findings demonstrated the potential of a deep-learning-based approach, particularly of models DINOv2-large (accuracy: 98.93%; precision: 84.52%; sensitivity: 78.00%; specificity: 99.57%; F1 score: 81.13%; AUROC: 0.97) and YOLOv8-m (accuracy: 97.59%; precision: 62.02%; sensitivity: 46.78%; specificity: 99.13%; F1 score: 53.33%; AUROC: 0.755; AUPR: 0.556) for their high metric values in intestinal parasite identification. Class-wise prediction showed high precision, sensitivity, and F1 scores for helminthic eggs and larvae due to more distinct morphology. Moreover, all models obtained a > 0.90 k score, which indicates a strong level of agreement compared with the medical technologists. The Bland–Altman analysis also presented the best agreement between FECT performed by medical technologist A and YOLOv4-tiny, while the MIF technique performed by medical technologist B and DINOv2-small demonstrated the best bias-free agreement, with mean differences of 0.0199 and −0.0080, and standard deviation differences of 0.6012 and 0.5588, respectively. Conclusions: The results highlight the potential of integrating a deep-learning-based approach into parasite identification. The models showcased superiority in automated detection, suggesting a significant leap toward improving diagnostic procedures for IPI. This hybridization could enhance early detection and diagnosis, facilitating timely and targeted interventions to reduce the burden of IPI through more effective management and prevention strategies.
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A deep contrastive learning-based image retrieval system for automatic detection of infectious cattle diseases
(2025-12-01)
Kittichai, Veerayuth
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Kaewthamasorn, Morakot
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Arnuphaprasert, Apinya
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Jomtarak, Rangsan
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Naing, Kaung Myat
Anaplasmosis, which is caused by Anaplasma spp. and transmitted by tick bites, is one of the most serious livestock animal diseases worldwide, causing significant economic losses as well as public health issues. Anaplasma marginale, a gram-negative intracellular obligate bacterium, can cause disease in cattle and other ruminants. Because of the insufficient quality of the slides, a microscopic diagnostic procedure is time-consuming and challenging to diagnose. Intra- and inter-rater variation is frequently imposed on by technicians who are underqualified and unexperienced. Alternatively, algorithms could support local employees in tracking disease transmission and quick action, especially in Thailand where this cattle disease is common. As a result, the study intends to create an automated tool based on a deep neural network linked with an image-retrieval procedure for recognizing infections in microscopic pictures. The Resnext-50 model, which serves as the embedding space’s backbone and is optimized by Triplet-Margin loss, outperforms, with averaged accuracy and specificity ratings of 91.30 percent and 92.83 percent, respectively. The model’s performance was also improved by a fine-tuned procedure between k-nearest neighbor and its normalized distance of each data point, including precision of 0.833 ± 0.134, specificity of 0.930 ± 0.054, recall of 0.838 ± 0.118, and accuracy of 0.915 ± 0.025, respectively. Five-fold cross-validation confirms that the trained model using the optimal k-nearest neighbor (kNN) for the image-based retrieval system, involving 12 images, prevents overfitting via dataset variations indicating areas under the receiver operating curve rankings ranging from 0.917 to 0.922. The image retrieval technique demonstrated in this research is a prototype for a variety of applications. The findings may aid in the early diagnosis of anaplasmosis infections in remote areas without access to veterinary care or costly molecular diagnostic tools.
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Enhancing mosquito classification through self-supervised learning
(2024-12-01)
Charoenpanyakul, Ratana
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Kittichai, Veerayuth
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Eiamsamang, Songpol
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Sriwichai, Patchara
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Pinetsuksai, Natchapon
Traditional mosquito identification methods, relied on microscopic observation and morphological characteristics, often require significant expertise and experience, which can limit their effectiveness. This study introduces a self-supervised learning-based image classification model using the Bootstrap Your Own Latent (BYOL) algorithm, designed to enhance mosquito species identification efficiently. The BYOL algorithm offers a key advantage by eliminating the need for labeled data during pretraining, as it autonomously learns important features. During fine-tuning, the model requires only a small fraction of labeled data to achieve accurate results. Our approach demonstrates impressive performance, achieving over 96.77% accuracy in mosquito image analysis, with minimized both false positives and false negatives. Additionally, the model’s overall accuracy, measured by the area under the ROC curve, surpasses 99.55%, highlighting its robustness and reliability. A notable finding is that fine-tuning with just 10% of labeled data produces results comparable to using the full dataset. This is particularly valuable for resource-limited settings with limited access to advanced equipment and expertise. Our model provides a practical solution for mosquito identification, overcoming the challenges of traditional microscopic methods, such as the time-consuming process and reliance on specialized knowledge in healthcare services. Overall, this model supports personnel in resource-constrained environments by facilitating mosquito vector density analysis and paving the way for future mosquito species identification methodologies.
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Enhance fashion classification of mosquito vector species via self-supervised vision transformer
(2024-12-01)
Kittichai, Veerayuth
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Kaewthamasorn, Morakot
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Chaiphongpachara, Tanawat
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Laojun, Sedthapong
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Saiwichai, Tawee
Vector-borne diseases pose a major worldwide health concern, impacting more than 1 billion people globally. Among various blood-feeding arthropods, mosquitoes stand out as the primary carriers of diseases significant in both medical and veterinary fields. Hence, comprehending their distinct role fulfilled by different mosquito types is crucial for efficiently addressing and enhancing control measures against mosquito-transmitted diseases. The conventional method for identifying mosquito species is laborious and requires significant effort to learn. Classification is subsequently carried out by skilled laboratory personnel, rendering the process inherently time-intensive and restricting the task to entomology specialists. Therefore, integrating artificial intelligence with standard taxonomy, such as molecular techniques, is essential for accurate mosquito species identification. Advancement in novel tools with artificial intelligence has challenged the task of developing an automated system for sample collection and identification. This study aims to introduce a self-supervised Vision Transformer supporting an automatic model for classifying mosquitoes found across various regions of Thailand. The objective is to utilize self-distillation with unlabeled data (DINOv2) to develop models on a mobile phone-captured dataset containing 16 species of female mosquitoes, including those known for transmitting malaria and dengue. The DINOv2 model surpassed the ViT baseline model in precision and recall for all mosquito species. When compared on a species-specific level, utilizing the DINOv2 model resulted in reductions in false negatives and false positives, along with enhancements in precision and recall values, in contrast to the baseline model, across all mosquito species. Notably, at least 10 classes exhibited outstanding performance, achieving above precision and recall rates exceeding 90%. Remarkably, when applying cropping techniques to the dataset instead of utilizing the original photographs, there was a significant improvement in performance across all DINOv2 models studied. This is demonstrated by an increase in recall to 87.86%, precision to 91.71%, F1 score to 88.71%, and accuracy to 98.45%, respectively. Malaria mosquito species can be easily distinguished from another genus like Aedes, Mansonia, Armigeres, and Culex, respectively. While classifying malaria vector species presented challenges for the DINOv2 model, utilizing the cropped images enhanced precision by up to 96% for identifying one of the top three malaria vectors in Thailand, Anopheles minimus. A proficiently trained DINOv2 model, coupled with effective data management, can contribute to the development of a mobile phone application. Furthermore, this method shows promise in supporting field professionals who are not entomology experts in effectively addressing pathogens responsible for diseases transmitted by female mosquitoes.
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Automatic classification and grading of canine tracheal collapse on thoracic radiographs by using deep learning
(2024-11-01)
Suksangvoravong, Hathaiphat
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Choisunirachon, Nan
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Tongloy, Teerawat
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Chuwongin, Santhad
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Boonsang, Siridech
Tracheal collapse is a chronic and progressively worsening disease; the severity of clinical symptoms experienced by affected individuals depends on the degree of airway collapse. Cutting-edge automated tools are necessary to modernize disease screening using radiographs across various veterinary settings, such as animal clinics and hospitals. This is primarily due to the inherent challenges associated with interpreting uncertainties among veterinarians. In this study, an artificial intelligence model was developed to screen canine tracheal collapse using archived lateral cervicothoracic radiographs. This model can differentiate between a normal and collapsed trachea, ranging from early to severe degrees. The you-only-look-once (YOLO) models, including YOLO v3, YOLO v4, and YOLO v4 tiny, were used to train and test data sets under the in-house XXX platform. The results showed that the YOLO v4 tiny-416 model had satisfactory performance in screening among the normal trachea, grade 1–2 tracheal collapse, and grade 3–4 tracheal collapse with 98.30% sensitivity, 99.20% specificity, and 98.90% accuracy. The area under the curve of the precision–recall curve was >0.8, which demonstrated high diagnostic accuracy. The intraobserver agreement between deep learning and radiologists was κ = 0.975 (P <.001), with all observers having excellent agreement (κ = 1.00, P <.001). The intraclass correlation coefficient between observers was >0.90, which represented excellent consistency. Therefore, the deep learning model can be a useful and reliable method for effective screening and classification of the degree of tracheal collapse based on routine lateral cervicothoracic radiographs.
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Identification of veterinary and medically important blood parasites using contrastive loss-based self-supervised learning
(2024-11-01)
Busayakanon, Supasuta
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Kaewthamasorn, Morakot
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Pinetsuksai, Natchapon
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Tongloy, Teerawat
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Chuwongin, Santhad
Background and Aim: Zoonotic diseases caused by various blood parasites are important public health concerns that impact animals and humans worldwide. The traditional method of microscopic examination for parasite diagnosis is labor-intensive, time-consuming, and prone to variability among observers, necessitating highly skilled and experienced personnel. Therefore, an innovative approach is required to enhance the conventional method. This study aimed to develop a self-supervised learning (SSL) approach to identify zoonotic blood parasites from microscopic images, with an initial focus on parasite species classification. Materials and Methods: We acquired a public dataset featuring microscopic images of Giemsa-stained thin blood films of trypanosomes and other blood parasites, including Babesia, Leishmania, Plasmodium, Toxoplasma, and Trichomonad, as well as images of both white and red blood cells. The input data were subjected to SSL model training using the Bootstrap Your Own Latent (BYOL) algorithm with Residual Network 50 (ResNet50), ResNet101, and ResNet152 as the backbones. The performance of the proposed SSL model was then compared to that of baseline models. Results: The proposed BYOL SSL model outperformed supervised learning models across all classes. Among the SSL models, ResNet50 consistently achieved high accuracy, reaching 0.992 in most classes, which aligns well with the patterns observed in the pre-trained uniform manifold approximation and projection representations. Fine-tuned SSL models exhibit high performance, achieving 95% accuracy and a 0.960 area under the curve of the receiver operating characteristics (ROC) curve even when fine-tuned with 1% of the data in the downstream process. Furthermore, 20% of the data for training with SSL models yielded ≥95% in all other statistical metrics, including accuracy, recall, precision, specification, F1 score, and ROC curve. As a result, multi-class classification prediction demonstrated that model performance exceeded 91% for the F1 score, except for the early stage of Trypanosoma evansi, which showed an F1 score of 87%. This may be due to the model being exposed to high levels of variation during the developmental stage. Conclusion: This approach can significantly enhance active surveillance efforts to improve disease control and prevent outbreaks, particularly in resource-limited settings. In addition, SSL addresses significant challenges, such as data variability and the requirement for extensive class labeling, which are common in biology and medical fields.
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A novel approach for identification of zoonotic trypanosome utilizing deep metric learning and vector database-based image retrieval system
(2024-05-15)
Kittichai, Veerayuth
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Sompong, Weerachat
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Kaewthamasorn, Morakot
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Sasisaowapak, Thanyathep
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Naing, Kaung Myat
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.
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The detection and classification of acute myeloid leukaemia blood cell images based on different YOLO approaches
(2024-04-01)
Naing, Kaung Myat
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Kittichai, Veerayuth
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Tongloy, Teerawat
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Chuwongin, Santhad
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Boonsang, Siridech
Medical image examination with a deep learning approach is greatly beneficial in the healthcare industry for faster diagnosis and disease monitoring. One of the popular deep learning algorithms such as you only look once (YOLO) developed for object detection is a successful state-of-the-art algorithm in real-time object detection systems. Although YOLO is continuously improving in the object detection area, there are still questions about how different YOLO versions compare in terms of performance. We utilize eight YOLO versions to classify acute myeloid leukaemia (AML) blood cells in image examinations. We also acquired the publicly available AML dataset from the cancer imaging archive (TCIA) which consists of expert-labeled single cell images. Data augmentation techniques are additionally applied to enhance and balance the training images in the dataset. The overall results indicated that eight types of YOLO approaches have outstanding performances of more than 90% in precision and sensitivity. In comparison, YOLOv4-tiny has a more reliable performance than the other seven approaches. Consistently, the YOLOv4-tiny also achieved the highest AUC score. Therefore, this work can potentially provide a beneficial digital rapid tool in the screening and evaluation of numerous haematological disorders.
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Automatic identification of medically important mosquitoes using embedded learning approach-based image-retrieval system
(2023-12-01)
Kittichai, Veerayuth
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Kaewthamasorn, Morakot
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Samung, Yudthana
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Jomtarak, Rangsan
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Naing, Kaung Myat
Mosquito-borne diseases such as dengue fever and malaria are the top 10 leading causes of death in low-income countries. Control measure for the mosquito population plays an essential role in the fight against the disease. Currently, several intervention strategies; chemical-, biological-, mechanical- and environmental methods remain under development and need further improvement in their effectiveness. Although, a conventional entomological surveillance, required a microscope and taxonomic key for identification by professionals, is a key strategy to evaluate the population growth of these mosquitoes, these techniques are tedious, time-consuming, labor-intensive, and reliant on skillful and well-trained personnel. Here, we proposed an automatic screening, namely the deep metric learning approach and its inference under the image-retrieval process with Euclidean distance-based similarity. We aimed to develop the optimized model to find suitable miners and suggested the robustness of the proposed model by evaluating it with unseen data under a 20-returned image system. During the model development, well-trained ResNet34 are outstanding and no performance difference when comparing five data miners that showed up to 98% in its precision even after testing the model with both image sources: stereomicroscope and mobile phone cameras. The robustness of the proposed—trained model was tested with secondary unseen data which showed different environmental factors such as lighting, image scales, background colors and zoom levels. Nevertheless, our proposed neural network still has great performance with greater than 95% for sensitivity and precision, respectively. Also, the area under the ROC curve given the learning system seems to be practical and empirical with its value greater than 0.960. The results of the study may be used by public health authorities to locate mosquito vectors nearby. If used in the field, our research tool in particular is believed to accurately represent a real-world scenario.

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