Kittichai, Veerayuth

Preferred name

Alternative Name

Kittichai, V.

Main Affiliation

Faculty of Medicine

veerayuth.ki@kmitl.ac.th

Now showing 1 - 10 of 21

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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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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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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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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.
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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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Superior Automatic Screening for Human Helminthic Ova by Using Self-supervised Learning Approach-Based Object Classification
(2023-01-01)
Pinetsuksai, Natchapon
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Kittichai, Veerayuth
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Jomtarak, Rangsan
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Jaksukam, Komgrit
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Tongloy, Teerawat
Human parasitic infections remain one of public health concerns for 1.5 billion people worldwide including Thailand. Conventional microscopic examination is a gold standard method and often used to identify the helminth ova and filariform larvae and also protozoa cyst in stool-dependent simple smear. The benefits of traditional techniques are diminished by time-consuming, complicated procedures, massive labor, and skilled and trained parasitologists. An automatically rapid screening of the most in need of treatment is considered to replace the conventional technique. Here, we aim to develop a deep convolutional residual network based self-supervised learning model to identify mostly common parasite ova in Thailand. Although small amounts of training data was used to train the proposed model, the result shows superior performance over 95% accuracy. As a result, low values of false positive and false negative based confusion matrix table found revealed the robustness of the proposed models. General accuracy of self-supervised learning based the area under a ROC curve proposed with greater than 94% is also support an outstanding model studied. Therefore, rank of 1% to 10% of fine-tuning data labelled used bring us about a comparable model to that of using a 100% labelled training data. These findings emphasize the transformative potential of the BYOL method for screening of parasitic infection, particularly in resource-limited settings where is a lack of supportive lab equipment and skilled parasitologists to manage a large amount of challenging data in the future.
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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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Simultaneous phenotyping of five Rh red blood cell antigens on a paper-based analytical device combined with deep learning for rapid and accurate interpretation
(2022-05-15)
Larpant, Nutcha
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Niamsi, Wisanu
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Noiphung, Julaluk
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Chanakiat, Wipada
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Sakuldamrongpanich, Tasanee
Both the ABO and Rhesus (Rh) blood groups play crucial roles in blood transfusion medicine. Herein, we report a simple and low-cost paper-based analytical device (PAD) for phenotyping red blood cell (RBC) antigens. Using this Rh typing format, 5 Rh antigens on RBCs can be simultaneously detected and macroscopically visualized within 12 min. The proposed Rh phenotyping relies on the presence or absence of hemagglutination in the sample zones after immobilizing the antibodies targeting each Rh antigen. The PAD was optimized in terms of filter paper type, antibodies, and distance of the visualization zone. In this study, the optimal conditions were Whatman filter paper Grade 4; anti-D, –C, -E, -c, and -e antibodies; RBC suspension of 30%; and a visualization zone of 1 cm above the sample zone. The accuracy of simultaneously phenotyping the five Rh RBC antigens in the blood samples (n = 4692) was 99.19%, comparable with the accuracy of the gold-standard tube method used by blood bank laboratories in several regions of Thailand. Furthermore, decision making based on this method can be assisted by deep learning. After implementing a two-stage objective detection algorithm (YOLO v4-tiny) and classification model (DenseNet-201), the ambiguous images (n = 48) were interpreted with 100% accuracy. The PAD integrated with customized-region convolutional neural networks can reduce the interpretation discrepancies in RBC antigen phenotyping in any laboratory.
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Kittichai, Veerayuth

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