Titijaroonroj, Taravichet

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Alternative Name

Titijaroonrog, Taravichet

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School of Information Technology

taravichet.ti@kmitl.ac.th

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Automatic Lymph Node Classification with Convolutional Neural Network
(2022-01-01)
Uthatham, Ason
;
Yodrabum, Nutcha
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Sinmaroeng, Chanya
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Titijaroonroj, Taravichet
Manual lymph node classification is a tedious and time-consuming task. It requires a histopathologist to discriminate a lymph node from other look-alike kinds of tissues. The lymph node is easily misunderstood with other tissues because its shape and color might be similar to the others tissue around it. To automate this task, we present an automatic lymph node classification with convolutional neural network (CNN). In addition, we compared eight existing CNNs to ensure that we discover the best architecture for discriminating lymph node. DenseNet architecture provided the highest performance among AlexNet, VGG, GoogLeNet, ResNet, SqueezeNet, MobileNet, and EfficientNet, the highest accuracy at 0.994 and an F1score of 0.996. DenseNet accomplished the highest performance from two advantages: (i) fewer parameters and (ii) Dense connectivity.
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Comparative performance of deep learning models and non-dermatologists in diagnosing psoriasis, dermatophytosis, and eczema
(2026-12-01)
Yodrabum, Nutcha
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Wongpraparut, Chanisada
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Titijaroonroj, Taravichet
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Chularojanamontri, Leena
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Bunyaratavej, Sumanas
Accurately differentiating scaly erythematous rashes among psoriasis, eczema, and dermatophytosis remains a clinical challenge, particularly for non-dermatologists. This study aimed to develop and evaluate deep learning models using macroscopic clinical images to classify these conditions and compare their performance with that of non-specialists. A total of 2940 images were sourced from public datasets, the Siriraj Dermatology databank, and newly collected images from Thai participants. Among sixteen evaluated models, the Swin demonstrated the best performance and interpretability. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations confirmed that the model focused on clinically relevant lesion features. Most importantly, in a pilot comparison, the Swin outperformed non-specialists in diagnostic accuracy. However, given the limited sample size of 30 images and 30 evaluators, these results should be interpreted as exploratory. Future studies with larger datasets and diverse clinician cohorts are warranted to confirm these findings and to support clinical integration.
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Automatic Thai Ticket Classification By Using Machine Learning For IT Infrastructure Company
(2022-01-01)
Khowongprasoed, Kraidet
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Titijaroonroj, Taravichet
Ticket classification is a process to define the category name of each ticket before assigning the resolution team to serve each ticket. It is an important process to support the customers inside and outside the company. It can make customer dissatisfaction if the processing time is high or delayed. Based on the recording data in 2019-2021 at the studying company, we found that the manual ticket classification got an error rate about 53 percent because the office workers misunderstand. To alleviate this problem, we propose the methodology for automatic Thai ticket classification by using Term Frequency-Inverse Document Frequency with Support Vector Machine. The experimental result shows that the performance of the proposed methodology is higher than the manual classification by 2 times or 41 percent.
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SkinHRNet: A Deep Learning Framework for Non-Contact Heart Rate Estimation and Arterial–Venous Sufficiency Status Classification from Short Skin Videos
(2026-01-01)
Traivinidsreesuk, Chetsadaporn
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Yodrabum, Nutcha
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Winaikosol, Kengkart
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Chaikangwan, Irin
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Prompattanapakdee, Jiraya
Traditional methods for monitoring flap health in reconstructive surgery are often invasive and rely on subjective assessment. This study addresses clinically motivated monitoring challenges by evaluating two tasks using a dataset of 1,018 short skin videos: average heart rate (HR) estimation under arterial–venous sufficiency conditions and arterial–venous sufficiency status classification across sufficiency and simulated insufficiency conditions. To address these challenges, we propose SkinHRNet, a deep learning–based approach for average HR estimation and arterial–venous sufficiency status classification from short skin videos. This work contributes a short-skin-video framework that combines HR-related signal estimation with arterial–venous sufficiency classification to support the two evaluated tasks under the controlled conditions considered in this study. For average HR estimation under arterial–venous sufficiency conditions, SkinHRNet achieved a mean absolute error (MAE) of 8.66 ± 4.85 BPM. For arterial–venous sufficiency status classification, it achieved an accuracy of 0.969 ± 0.02 across the evaluated sufficiency and simulated insufficiency conditions. These findings indicate that SkinHRNet may serve as an initial research prototype for further investigation of short-video-based non-contact assessment under controlled arterial–venous sufficiency and simulated insufficiency conditions.
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Chertify: Wood Identification-Based Mobile Cross-platform by Deep Learning Technique
(2022-01-01)
Wongpoo, Teerasak
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Sriwan, Wannamongkol
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Titijaroonroj, Taravichet
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Jamsri, Pornsuree
Thailand’s economic trees are counted as one of its most valuable domestic assets and well known internationally as a high quality natural wood resource. However, there is a need for basic wood identification whether or not for a required certificate by individuals, entrepreneurs, and organizations. Currently, the wood identification process is manually accomplished only by an expert at the Forest Research and Development Office, the Royal Thai Forest Department. This is a time consuming complex process for two reasons–required experience and limited experts. Given the complexity of wood identification, a new approach is offered, namely, to identify different types of wood with an image from a smartphone. The researcher initially proposes a mobile application, “Chertify”, that has five features (login, wood check, wood check history, manual, and wood knowledge). This app can serve both iOS and Android platforms and targets the general user. Chertify aims to simplify identification of an economic wood type by combining deep learning technology with an actual wood image on a Smartphone. The selected deep learning algorithm will be applied to 258 trained group images of seven wood types based on highest accuracy and lowest standard deviation. Chertify relies on a handcrafted method (HOG and SVM) and a learning-based method (Alexnet) with accuracy of 69.4% and 84.73% and SD at 5.37% and 3.07% values, respectively.
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Drugtionary: Drug Pill Image Detection and Recognition Based on Deep Learning
(2022-01-01)
Pornbunruang, Naphat
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Tanjantuk, Veerapong
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Titijaroonroj, Taravichet
Drugtionary, which is a mobile application, is developed to support people who lack medical understanding and avoid taking the wrong drug. It consists of four main features including (i) sign-up, (ii) managing profile and medication history, (iii) viewing medication information, and (iv) managing the schedule. For viewing medication information, there are three ways to retrieve the drug information–(i) text search, (ii) chatbot, and image search. We use string search and DialogFlow for text search and chatbot, respectively, whereas deep learning technique for image detection and recognition is used to search the given drug pill image. The experimental result shows that the model generated from the CenterNet method is suitable when compared to the Faster-RCNN, RetinaNet, Yolo, and SSD on our drug pill dataset. Moreover, our application is constructed by using React and React Native technology. All data are stored in the MongoDB database.
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Blood Vessels Detection by Regional-based CNN for CT Scan of Lower Extremities
(2022-01-01)
Sakunpaisanwari, Littikrai
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Yodrabum, Nutcha
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Sirirapisit, Tanongchai
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Titijaroonroj, Taravichet
Blood vessels on computed tomography (CT) scan images are difficult to identify and discriminate between vessels and noise because blood vessels are not only small and shapeless, but its location can also be inconsistent. This is a challenge of object detection. We proposed an automatic blood vessel detection method based on YOLOv3 for object detection from CT scan of lower extremities. This work focused on detecting four main arteries: popliteal, anterior tibial, posterior tibial, and peroneal arteries. To obtain the best architecture for blood vessel detection, we evaluated and compared the performances of seven region-based CNN architectures: Faster R-CNN, Cascade R-CNN, Mask R-CNN, RetinaNet, YOLOv3, CornerNet, and Centernet. Experimental results show that the best architecture was YOLOv3 with precision, recall, and f1-score of 0.982, 0.954, and 0.968, respectively. Good accomplishment of YOLOv3 came from skip connections, multi-scale feature map, and anchor generated by k-means clustering.
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Automated Verification of English Proficiency Test Scores Using OCR for Graduation Qualification
(2025-01-01)
Titijaroonroj, Taravichet
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Maliwan, Thitiwut
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Jaimetha, Nattakamon
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Wattanacheep, Bhattarabhorn
An algorithm is proposed to verify English proficiency scores using OCR for graduation qualification, aiming to reduce errors and workload in the current manual verification process of officer. Score reports in image or PDF format, including TOEIC and KMITL-TEP, are processed using two deep learning-based document classifiers that identify the test type and score type prior to OCR execution. Preprocessing techniques such as noise reduction, contrast enhancement, and skew correction are applied to improve OCR accuracy. Three OCR models-Tesseract, TrOCR, and EasyOCR-are evaluated for text extraction performance. The extracted textual data are then converted into structured JSON, enabling automated rule-based comparison against graduation criteria. Evaluation performance is measured using Accuracy, F1-Score, Character Error Rate (CER), and Word Error Rate (WER). Experimental results show that the proposed system achieves 99% accuracy, demonstrating both high reliability and adaptability to institutional scoring standards. The integration of OCR significantly reduces processing time while maintaining flexibility to accommodate future changes in assessment policies.
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LymphoNet: A Deep Learning for Lymph Node Detection from Histological Image
(2024-01-01)
Uthatham, Ason
;
Yodrabum, Nutcha
;
Sinmaroeng, Chanya
;
Chaikangwan, Irin
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Titijaroonroj, Taravichet
Identifying lymph nodes within a lymph node flap is crucial for precise lymph node quantification. Even when observed under a microscope, this task is known for being extremely challenging and susceptible to misidentification. Histopathology is the most reliable method for detecting lymph nodes in histopathological slides, but it is a very time-consuming and labor-intensive technique. In particular, the anatomical intricacy and significant clinical implications of the submental lymph node flap model require precise identification to ensure an effective count. Emerging deep learning techniques have shown promising capabilities for automating such meticulous tasks, potentially enhancing diagnostic efficacy and accuracy. This paper proposed LymphoNet, a deep learning model for automated detection of submental lymph nodes in histopathological slides, aiming to enhance diagnostics and reduce labor. We compared LymphoNet's performance with other models. LymphoNet demonstrated the performance, accurately identifying lymph nodes with high precision and recall, and achieving a strong F1 score. It effectively identified lymph node regions, minimizing false positives, as evidenced by a low Mean Absolute Error with non-lymph node tissues. This accuracy is necessary for lymph node flap studies in lymphedema treatment, promising to accelerate histological analysis and support pathologists and anatomists. In conclusion, LymphoNet represents a significant advancement in histopathological examination, offering precise lymph node detection that could become an essential tool for surgical planning in lymphedema management, enhancing study efficiency and treatment outcomes.
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Chatbot-Powered Orchid Leaf Disease Classification and Management for Improved Farming
(2023-01-01)
Santisarn, Keerati
;
Titijaroonroj, Taravichet
Orchids are important economic plants in Thailand. However, orchid diseases are one of the obstacles affecting the quantity and quality of orchid yields. Classifying orchid leaf diseases requires expertise and might be difficult to follow and manage once discovered. In this research, our objective is to develop an orchid leaf disease classification model and integrate it into the chatbot with management features such as notifying and tracking when a disease is discovered. A dataset was collected from a studying orchid farm in Nakhon Pathom, Thailand, and categorized into two classes: normal orchid leaves and orchid leaves infected with yellow leaf spot disease. Well-known CNN architectures, such as DenseNet201, GoogLeNet, MobileNetV2, etc., are used to develop classification models and measure accuracy. The results show that using MobileNetV2 achieves the highest accuracy of 98%. Subsequently, the orchid leaf disease chatbot system was designed and implemented on the LINE platform.

Titijaroonroj, Taravichet

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