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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 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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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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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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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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Development of Self-Supervised Learning with Dinov2-Distilled Models for Parasite Classification in Screening
(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
At present, parasitic infections in humans, such as intestinal parasitic infections and soil-transmitted helminth (STH) infection, remain a public health concern, with screening methods that are simple but time-consuming and require parasitology experts. Microscopy images are increasingly being used to aid diagnosis but creating labels for supervised learning (SL) is a time-consuming, labor-intensive, and costly process. Self-supervised learning (SSL) is a deep learning approach that aims to train models to represent features in unlabeled datasets using automatically generated labels or annotations from the data itself, rather than explicitly labeled human-labeled labels. It is an appropriate method to address the challenges associated with the difficulty of labeling large datasets. A pretrained model that has learned useful data representations from an SSL task is fine-tuned using labeled data to perform well on a specific downstream task. DINOv2 is an SSL model based on the Vision Transformer (ViT) architecture. In this study, we aim to create a model for screening for helminth egg infection using a fine-tuned Dinov2 with a classification layer head to demonstrate that dataset sizes of 1% and 10% are sufficient when compared to SL model. Rather than SL, which requires a significant amount of human data labeling and is generally impractical, the model developed in this study is expected to be used in active surveillance in the future.
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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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Multi-wavelength Čerenkov radiations in a microring resonator in combination with two gratings
(2015-07-25)
Jomtarak, Rangsan
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Yupapin, Preecha P.
In this paper, the Čerenkov radiation of light pulse in a microring and gratings is simulated and investigated. The system design consists of a two-defect grating incorporating a microring, connected with a uniform grating. In simulation, the continuous wave (CW) light pulse with wavelength centered at 1.55 μm is input into the microring device via the two-defect grating. The resonant outputs from the two-defect grating propagate through the microring and uniform grating, where the time delays of those two input pulses with different wavelengths through the system are distinguished by the output uniform grating. From the obtained resonant output pulses, we find that the red-shifted and blue-shifted Čerenkov pulses are observed. In applications, such a proposed system can be used to form two different optical delay pulses, in which the change in Čerenkov radiation of them, i.e., time delay within a microring device system, can be useful for Čerenkov radiation imaging and sensing applications.
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Doppler shift velocimetry using two point probe nested-microring resonators
(2015-03-05)
Jomtarak, Rangsan
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Yupapin, Preecha P.
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Ali, Jalil
In this paper, a micro Doppler shift velocimeter using two nested-microring resonators incorporating two gratings is designed and simulated. Two wavelength light pulse can be generated by a single input wavelength source after travelling through the two defect grating. Whispering gallery mode of light pulses are localized within the nested-microring resonators, which can be used to form the two point probe Doppler shift velocimeter. The shifted frequency (velocity) can be obtained by the difference of optical path length between two point probes, where the relative velocity of Doppler frequency shift velocimeter can be obtained. The light absorption behaviors can also be investigated by the different light output spectrum. The obtained simulation results shows that the relative velocities of the reflection and transmission light intensities are ranged from 10<sup>4</sup> m/s to 10<sup>5</sup> m/s, which can be useful for Doppler frequency shift velocimeter and sensors, where the micro-optical device and multi-functions are the advantages.
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Atom bottom-up manipulation controlled by light for microbattery use
(2012-09-17)
Zainol, Farrah Dilla
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Jomtarak, Rangsan
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Daud, Suzairi
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Teeka, Chat
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Ali, Jalil
In this paper, we propose a new design of the atom bottom-up technique that uses an optical trapping tool to form the atom trapping layer within a thin-film grating. By using a PANDA ring resonator, where atoms can be trapped, pumped, and controlled by light, the trapped atoms/molecules can be selected, filtered, and embedded within the required thin-film grating layers to manufacture nanobattery. In application, P-type or N-type atom can be prepared, trapped, and embedded within the desired thin-film layers, and finally, the microbattery can be manipulated. The theoretical background of light pulse in a PANDA ring resonator is also reviewed. © 2002-2012 IEEE.

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