Kittichai, Veerayuth

Preferred name

Alternative Name

Kittichai, V.

Main Affiliation

Faculty of Medicine

veerayuth.ki@kmitl.ac.th

Now showing 1 - 10 of 12

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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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Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks
(2021-12-01)
Kittichai, Veerayuth
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Kaewthamasorn, Morakot
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Thanee, Suchansa
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Jomtarak, Rangsan
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Klanboot, Kamonpob
The infection of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to the poultry industry because it causes economic loss in both the quality and quantity of meat and egg production. Computer-aided diagnosis has been developed to automatically identify avian malaria infections and classify the blood infection stage development. In this study, four types of deep convolutional neural networks, namely Darknet, Darknet19, Darknet19-448 and Densenet201 are used to classify P. gallinaceum blood stages. We randomly collected a dataset of 12,761 single-cell images consisting of three parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. The study mainly compared several image classification models and used both qualitative and quantitative data for the evaluation of the proposed models. In the model-wise comparison, the four neural network models gave us high values with a mean average accuracy of at least 97%. The Darknet can reproduce a superior performance in the classification of the P. gallinaceum development stages across any other model architectures. Furthermore, the Darknet has the best performance in multiple class-wise classification, with average values of greater than 99% in accuracy, specificity, and sensitivity. It also has a low misclassification rate (< 1%) than the other three models. Therefore, the model is more suitable in the classification of P. gallinaceum blood stages. The findings could help us create a fast-screening method to help non-experts in field studies where there is a lack of specialized instruments for avian malaria diagnostics.
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Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
(2023-10-01)
Kittichai, Veerayuth
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Kaewthamasorn, Morakot
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Thanee, Suchansa
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Sasisaowapak, Thanyathep
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Naing, Kaung Myat
Trypanosomiasis is a significant public health problem in several regions across the world, including South Asia and Southeast Asia. The identification of hotspot areas under active surveillance is a fundamental procedure for controlling disease transmission. Microscopic examination is a commonly used diagnostic method. It is, nevertheless, primarily reliant on skilled and experienced personnel. To address this issue, an artificial intelligence (AI) program was introduced that makes use of a hybrid deep learning technique of object identification and object classification neural network backbones on the in-house low-code AI platform (CiRA CORE). The program can identify and classify the protozoan trypanosome species, namely Trypanosoma cruzi, T. brucei, and T. evansi, from oil-immersion microscopic images. The AI program utilizes pattern recognition to observe and analyze multiple protozoa within a single blood sample and highlights the nucleus and kinetoplast of each parasite as specific characteristic features using an attention map. To assess the AI program's performance, two unique modules are created that provide a variety of statistical measures such as accuracy, recall, specificity, precision, F1 score, misclassification rate, receiver operating characteristics (ROC) curves, and precision versus recall (PR) curves. The assessment findings show that the AI algorithm is effective at identifying and categorizing parasites. By delivering a speedy, automated, and accurate screening tool, this technology has the potential to transform disease surveillance and control. It could also assist local officials in making more informed decisions on disease transmission-blocking 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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Automatic recognition of parasitic products in stool examination using object detection approach
(2022-01-01)
Naing, Kaung Myat
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Boonsang, Siridech
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Chuwongin, Santhad
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Kittichai, Veerayuth
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Tongloy, Teerawat
Background. Object detection is a new artificial intelligence approach to morpho- logical recognition and labeling parasitic pathogens. Due to the lack of equipment and trained personnel, artificial intelligence innovation for searching various parasitic products in stool examination will enable patients in remote areas of undeveloped countries to access diagnostic services. Because object detection is a developing approach that has been tested for its effectiveness in detecting intestinal parasitic objects such as protozoan cysts and helminthic eggs, it is suitable for use in rural areas where many factors supporting laboratory testing are still lacking. Based on the literatures, the YOLOv4-Tiny produces faster results and uses less memory with the support of low- end GPU devices. In comparison to the YOLOv3 and YOLOv3-Tiny models, this study aimed to propose an automated object detection approach, specifically the YOLOv4- Tiny model, for automatic recognition of intestinal parasitic products in stools. Methods. To identify protozoan cysts and helminthic eggs in human feces, the three YOLO approaches; YOLOv4-Tiny, YOLOv3, and YOLOv3-Tiny, were trained to recognize 34 intestinal parasitic classes using training of image dataset. Feces were processed using a modified direct smear method adapted from the simple direct smear and the modified Kato-Katz methods. The image dataset was collected from intestinal parasitic objects discovered during stool examination and the three YOLO models were trained to recognize the image datasets. Results. The non-maximum suppression technique and the threshold level were used to analyze the test dataset, yielding results of 96.25% precision and 95.08% sensitivity for YOLOv4-Tiny. Additionally, the YOLOv4-Tiny model had the best AUPRC performance of the three YOLO models, with a score of 0.963. Conclusion. This study, to our knowledge, was the first to detect protozoan cysts and helminthic eggs in the 34 classes of intestinal parasitic objects in human stools.
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Images Retrieval and Classification for Acute Myeloid Leukemia Blood Cell Using Deep Metric Learning
(2023-01-01)
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
Deep metric learning-based image retrieval systems have recently been used in medical applications because they provide clinically relevant information-based similar images based on prior knowledge. Although train examiners and deep learning models successfully analyze leukocyte cells, there are still numerous difficult challenges due to biological variation, time constraints, and a variety of image-related aspects. In this study, we propose a deep metric learning model-based image retrieval and classification system for acute myeloid leukemia blood cells to address these issues and assist physicians. The proposed model utilizes the pre-trained ResNet-34 model as the backbone network, embedding loss with multi similarity miner, and M-Per-Class sampling strategy to learn an embedding function. The five embedding losses were also applied to compare the four performances in order to determine the best loss-based model. Based on the best loss-based model, the class-wise precision and sensitivity using a neighborhood size are also presented. The results show that the contrastive loss-based deep metric learning model achieved the highest precision of 94.90%, sensitivity of 94.85%, specificity of 99.64%, and accuracy of 99.32% in model comparison. Except for a few failures in small classes, the class-wise precision and sensitivity scores looked to be impressive in all classes. Therefore, this proposed system can highly be effective in screening and diagnosing of AML-related white blood cell stages that cause serious cancer.
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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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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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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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Mobile Bot Application for Identification of Trypanosoma evansi Infection through Thin-Blood Film Examination Based on Deep Learning Approach
(2023-01-01)
Jomtarak, Rangsan
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Kittichai, Veerayuth
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Kaewthamasorn, Morakot
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Thanee, Suchansa
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Arnuphapprasert, Apinya
Trypanosomiasis caused Trypanosoma evansi is current public health concern especially, in south Asia and Southeast Asia. Although polymerase chain reaction is currently used as a standard method, the techniques required skilled personnel, were performed in multiple steps, and required expensive instruments. Fundamental microscopic approach also has limitation in use by facing both inter- and intra-variability of interpretation by examiners. New automatic tool with the microscopic examination is needed. The study aimed to develop the mobile application-based YOLO neural network algorithms to predict T. evansi blood stages from thin-blood film examination. YOLO v4 tiny model is outperformed to localize and classify unseen images with the best performance at 95% of sensitivity, specificity, precision, accuracy and F1 score, respectively, with less misclassification rate than 5%. Simulation implementation platform, calling CiRA bot, give the empirical result and reliably comparable to that from the computational experiment studied with the area under ROC and precision-recall curves as 0.964 and 0.962, respectively. The result obtained from the CIRA bot platform is good enough for further distribution in field site. In the future, the study could contribute human and animal public health staff to simply identify the unicellular parasitic flagellate infection and also benefit them for designing the strategy in prevention and treatment of the disease.

Kittichai, Veerayuth

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