Browsing by Author "Suchansa Thanee"

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    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.th
    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. © 2021, The Author(s).
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    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.th
    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. © 2023 IEEE.
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    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.th
    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. © 2023 JoVE Journal of Visualized Experiments.