Treebupachatsakul, Treesukon
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Preferred name
Treebupachatsakul, Treesukon
Alternative Name
Treebupachatsakul, T.
Main Affiliation
Email
treesukon.tr@kmitl.ac.th
20 results
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Item type:Publication, The Comparison of Deep Learning Model Efficiency for Classification of Oral White Lesions(2022-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Pressure Swing Absorption Oxygen Concentrator equipped with Remote Monitoring Pulse Oximeter(2021-01-01); The new COVID-19 disease was first identified in China at the end of 2019 and has spread rapidly all over the world. It has been projected that, by March 2021, the number of infections could reach 300 million cases and over two million deaths. One of the main implications for COVID-19 patients is pneumonia where the lung is infected, hence patients suffering from insufficient oxygen in the blood. As the number of COVID-19 cases have significantly increased, the demand for oxygen generators have also skyrocketed. This research concerns the design and construction of emergency low-cost oxygen concentrators used for mild COVID-19 symptoms, of which are forced to be treated at home. Our absorption-based oxygen concentrator uses zeolite packed in a sieve canister. An Oil-free compressor is then used to pump air in. Zeolite will absorb nitrogen from the air leaving oxygen free to travel towards the outlet. To evaluate the treatment, we have equipped the system with a pulse oximeter to measure the percent saturation oxygen, pulse rate and temperature. To prevent COVID-19 infections between patients and caretakers, we have designed an android application to remotely control the oxygen concentrator. Experiment has shown that our emergency low-cost oxygen concentrator can supply oxygen with an 85% purity rate. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Classification model for predicting inflammation of the urinary bladder and acute nephritis of the renal pelvis(2022-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An ultra-violet sterilization robot for disinfection(2019-07-01); ; Ultraviolet (UV) sterilization technology is used to aid in reduction of microorganisms that may remain on the surfaces after a standard cleaning to the minimum number. Our research team developed a UV robot or UV bot for sterilization in an operating or a patient room. Our UV bot has three 19.3-watt of UV lamps mounted on top of the UV bot platform covering 360° direction. Our UV bot employed an embedded system based on a Raspberry Pi to aid in navigation to avoid obstacles. In addition, we tested the effectiveness of eliminating Staphylococcus Aureus bacteria sample plates located 35 cm away from our UV bot to be within 8 seconds after UV light exposure. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deep Learning to Classify Bacterial Species in the same Genus(2024-01-01); Bacterial strains in the same genus share highly similar morphology, gram-staining characteristics, colony sizes, and spatial arrangements. Therefore, identifying them by deep learning can be quite challenging. This study aimed to assess the classification of 7 species of bacteria from 2 genera of Bacillus and Vibrio by using 8 Convolutional Neural Network (CNN) models. We implemented Python programming along with Keras API within the Jupyter Notebook. The models were constructed and evaluated under unbalanced and balanced datasets by augmentation (rotation, flip, etc.). Transfer learning with fine-tuning, and pre-processing of mixup and label smoothing were also applied to reduce overfitting and enhance generalization. Based on the experimental results on private dataset, the results of InceptionResNetV2 emerged as the top-performing model with a notable accuracy of 82.8%, 88.6% precision, 78.4% recall, and 78.0% F1-score when label smoothing was applied at 0.5 on balanced dataset. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Bacteria Classification using Image Processing and Deep learning(2019-06-01); An automizing process for bacteria recognition becomes attractive to reduce the analyzing time and increase the accuracy of diagnostic process. This research study possibility to use image classification and deep learning method for classify genera of bacteria. We propose the implementation method of bacteria recognition system using Python programing and the Keras API with TensorFlow Machine Learning framework. The implementation results have confirmed that bacteria images from microscope are able to recognize the genus of bacterium. The experimental results compare the deep learning methodology for accuracy in bacteria recognition standard resolution image use case. Proposed method can be applied the high-resolution datasets till standard resolution datasets for prediction bacteria type. However, this first study is limited to only two genera of bacteria. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Microfluidic channel from gelatin using laser printer(2020-10-29) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Force Sensor for Measuring Plantar Pressure(2019-11-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Real-Time White Blood Cell Classification with YOLO(2025-01-01); White blood cell (WBC) classification plays a crucial role in diagnosing various hematological conditions, including infections, immune disorders, and leukemia. This study presents an automated approach for WBC detection and classification using the YOLOv5 deep learning model. The system integrates a 1.3MP microscope camera with a stepper motor-driven platform for real-time imaging and classification. The dataset consists of five WBC types: basophils, eosinophils, lymphocytes, monocytes, and neutrophils, with image enhancement and data augmentation applied to improve model performance. The trained YOLOv5 model achieved a classification accuracy of 92.61% and a validation accuracy of 95.86%, demonstrating high precision and recall in WBC identification. The results indicate that this system can effectively automate WBC analysis, reducing manual effort and improving diagnostic accuracy. This approach has potential applications in clinical hematology, offering a rapid and reliable method for WBC classification. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Smart eye-tracking system(2018-05-30); This project is a smart eye tracking system which is designed for people with disabilities and elderly people. The concept of this research is to apply eye movement to control appliances, wheelchair and communicate with caretaker. This system comprises four components, imaging processing module, wheelchair-controlled module, appliances-controlled module and SMS manager module. The image processing module consists of webcam and C++ customized image processing, the eye movement image is captured and transmitted to Raspberry Pi microcontroller for processing with OpenCV to derive the coordinate of eye ball. The coordinate of eye ball is utilized for cursor control on the Raspberry Pi screen to control the system. Besides the eye movement, the eye blink is applied in this system for entering a command as when you press Enter button on keyboard. The wheelchair-controlled module is a cradle with two servos that can be moved to two dimensions and also adaptable to other wheelchair joysticks. This system also remotely controls some appliances and communicate with caretaker via send message to smartphone.