Browsing by Author "Morakot Kaewthamasorn"
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Item A deep contrastive learning-based image retrieval system for automatic detection of infectious cattle diseases(Springer Nature, 2025) Veerayuth Kittichai; Morakot Kaewthamasorn; Apinya Arnuphaprasert; Rangsan Jomtarak; Kaung Myat Naing; Teerawat Tongloy; Santhad Chuwongin; Siridech Boonsang; S. Boonsang; Department of Electrical Engineering, School of Engineering, King MongkutÕs Institute of Technology Ladkrabang, Bangkok, Thailand; email: siridech.bo@kmitl.ac.thAnaplasmosis, 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 spaceÕs 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 modelÕs 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. © The Author(s) 2024.Item Automatic identification of medically important mosquitoes using embedded learning approach-based image-retrieval system(Nature Research, 2023) Veerayuth Kittichai; Morakot Kaewthamasorn; Yudthana Samung; Rangsan Jomtarak; Kaung Myat Naing; Teerawat Tongloy; Santhad Chuwongin; Siridech Boonsang; S. Boonsang; Department of Electrical Engineering, School of Engineering, King MongkutÕs Institute of Technology Ladkrabang, Bangkok, Thailand; email: Siridech.Bo@kmitl.ac.thMosquito-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 proposedÑtrained 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. © 2023, The Author(s).Item Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks(Nature Research, 2021) Veerayuth Kittichai; Morakot Kaewthamasorn; Suchansa Thanee; Rangsan Jomtarak; Kamonpob Klanboot; Kaung Myat Naing; Teerawat Tongloy; Santhad Chuwongin; Siridech Boonsang; S. Boonsang; Department of Electrical Engineering, School of Engineering, King MongkutÕs Institute of Technology Ladkrabang, Bangkok, Thailand; email: Siridech.bo@kmitl.ac.thThe 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. © 2021, The Author(s).Item Mobile Bot Application for Identification of Trypanosoma evansi Infection through Thin-Blood Film Examination Based on Deep Learning Approach(Institute of Electrical and Electronics Engineers Inc., 2023) Rangsan Jomtarak; Veerayuth Kittichai; Morakot Kaewthamasorn; Suchansa Thanee; Apinya Arnuphapprasert; Kaung Myat Naing; Teerawat Tongloy; Siridech Boonsang; Santhad Chuwongin; S. Chuwongin; College of Advanced Manufacturing Innovation, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand; email: santhad.ch@kmitl.ac.thTrypanosomiasis 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. © 2023 IEEE.Item Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model(Journal of Visualized Experiments, 2023) Veerayuth Kittichai; Morakot Kaewthamasorn; Suchansa Thanee; Thanyathep Sasisaowapak; Kaung Myat Naing; Rangsan Jomtarak; Teerawat Tongloy; Santhad Chuwongin; Siridech Boonsang; V. Kittichai; Faculty of Medicine, King MongkutÕs Institute of Technology Ladkrabang, Thailand; email: Veerayuth.ki@kmitl.ac.th; S. Boonsang; Department of Electrical Engineering, School of Engineering, King MongkutÕs Institute of Technology Ladkrabang, Thailand; email: siridech.bo@kmitl.ac.thTrypanosomiasis 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. © 2023 JoVE Journal of Visualized Experiments.