Titijaroonroj, Taravichet

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Titijaroonrog, Taravichet

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School of Information Technology

taravichet.ti@kmitl.ac.th

Now showing 1 - 8 of 8

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Iteration-free Bi-dimensional empirical mode decomposition and its application
(2017-09-01)
Titijaroonroj, Taravichet
;
Woraratpanya, Kuntpong
powerful methods for decomposing non-linear and nonstationary signals without a prior function. It can be applied in many applications such as feature extraction, image compression, and image filtering. Although modified BEMDs are proposed in several approaches, computational cost and quality of their bi-dimensional intrinsic mode function (BIMF) still require an improvement. In this paper, an iteration-free computation method for bi-dimensional empirical mode decomposition, called iBEMD, is proposed. The locally partial correlation for principal component analysis (LPC-PCA) is a novel technique to extract BIMFs from an original signal without using extrema detection. This dramatically reduces the computation time. The LPC-PCA technique also enhances the quality of BIMFs by reducing artifacts. The experimental results, when compared with state-of-The-Art methods, show that the proposed iBEMD method can achieve the faster computation of BIMF extraction and the higher quality of BIMF image. Furthermore, the iBEMD method can clearly remove an illumination component of nature scene images under illumination change, thereby improving the performance of text localization and recognition.
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Comparative performance of deep learning models and non-dermatologists in diagnosing psoriasis, dermatophytosis, and eczema
(2026-12-01)
Yodrabum, Nutcha
;
Wongpraparut, Chanisada
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Titijaroonroj, Taravichet
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Chularojanamontri, Leena
;
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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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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Intelligent approach to automated star-schema construction using a knowledge base
(2021-11-15)
Sanprasit, Non
;
Jampachaisri, Katechan
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Titijaroonroj, Taravichet
;
Kesorn, Kraisak
Most data-warehouse construction processes are performed manually by experts, which is laborious, time-consuming, and prone to error. Furthermore, special knowledge is required to design complex multidimensional models, such as a star schema. This predicament has motivated computer scientists to propose automation techniques to generate such models. For this reason, we present a new strategy that incorporates knowledge-based models into a framework, named the Semantic-based Star-schema Designer, that assists the automation of star schema construction. Our models provide reasoning capabilities needed by star schema designs, including those that can disambiguate heterogeneous terms, detect appropriate data types and attribute sizes, and organize data hierarchies to support online analytical processes. We also propose strategies to overcome the uncertainty arising when attribute names are not available in the data source. The names of unknown attributes are thus predicted using an arithmetic coding technique to infer column names. Our system also generates star schema from semi-structured data (e.g., comma-separated-value files and spreadsheets), which do not provide primary keys, foreign keys, or relationship cardinalities between tables. Our framework facilitates star schema construction and their relationship information without human intervention using homegrown algorithms. Experiments demonstrate that our technique predicts column names and data types that enable the effective generation of star schema better than baseline approaches.
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LymphoNet: A Deep Learning for Lymph Node Detection from Histological Image
(2024-01-01)
Uthatham, Ason
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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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A semantic approach to automated design and construction of star schemas
(2021-07-12)
Sanprasit, Non
;
Titijaroonroj, Taravichet
;
Kesorn, Kraisak
Designing a star schema is a complex and time-consuming process requiring an expert to perform several tasks such as denormalization, dimension design, and construction of fact tables. This study presents a method to automatically design and generate star schema models, or so-called multidimensional models. We first introduce a method to incorporate a novel knowledge-based framework to enable an automation system to construct dimensional and fact tables as well as measures, which are the key elements of star schema models. The proposed framework provides a capability of column name identification using the arithmetic coding approach and measures identification using a natural language processing framework (NLP), resulting in dimensions and fact tables being constructed automatically without human intervention. Although the current version of our system is limited to reading data from semi-structured datasets such as CSV files and spreadsheets, the experimental results demonstrate that our framework can generate a star schema effectively, and can support online analytical processing (OLAP) operations. The experimental results show that our method is superior to other conventional approaches, achieving 96.67% accuracy for numerical data, higher than any of the prior models used for comparison.
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Deep learning-based classification of lymphedema and other lower limb edema diseases using clinical images
(2025-12-01)
Lewsirirat, Thanat
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Titijaroonroj, Taravichet
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Apichonbancha, Sirin
;
Uthatham, Ason
;
Suwanruangsri, Veera
Lymphedema is a chronic condition characterized by lymphatic fluid accumulation, primarily affecting the limbs. Its diagnosis is challenging due to symptom overlap with conditions like chronic venous insufficiency (CVI), deep vein thrombosis (DVT), and systemic diseases, often leading to diagnostic delays that can extend up to ten years. These delays negatively impact patient outcomes and burden healthcare systems. Conventional diagnostic methods rely heavily on clinical expertise, which may fail to distinguish subtle variations between these conditions. This study investigates the application of artificial intelligence (AI), specifically deep learning, to improve diagnostic accuracy for lower limb edema. A dataset of 1622 clinical images was used to train sixteen convolutional neural networks (CNNs) and transformer-based models, including EfficientNetV2, which achieved the highest accuracy of 78.6%. Grad-CAM analyses enhanced model interpretability, highlighting clinically relevant features such as swelling and hyperpigmentation. The AI system consistently outperformed human evaluators, whose diagnostic accuracy plateaued at 62.7%. The findings underscore the transformative potential of AI as a diagnostic tool, particularly in distinguishing conditions with overlapping clinical presentations. By integrating AI with clinical workflows, healthcare systems can reduce diagnostic delays, enhance accuracy, and alleviate the burden on medical professionals. While promising, the study acknowledges limitations, such as dataset diversity and the controlled evaluation environment, which necessitate further validation in real-world settings. This research highlights the potential of AI-driven diagnostics to revolutionize lymphedema care, bridging gaps in conventional methods and supporting healthcare professionals in delivering more precise and timely interventions. Future work should focus on external validation and hybrid systems integrating AI and clinical expertise for comprehensive diagnostic solutions.
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Spatial-Frequency Redistribution-based Saliency Region Detection for Thai Text Localisation
(2022-03-01)
Titijaroonroj, Taravichet
;
Suttapakti, Ungsumalee
;
Nunsong, Walairach
Saliency region detection plays an important role in computer vision applications and related areas, such as human fixation, figure-ground separation, face detection, and image compression. Nevertheless, state-of-the-art methods can moderately detect the saliency regions of a psychological pattern dataset. Therefore, this paper proposes a spatial-frequency redistribution (SFR) method to improve the efficiency of the detection of the saliency regions. The proposed SFR method consists of two main procedures: (i) adaptive cosine image construction-and-implantation and (ii) saliency region detection using complete multiple-object-implanted images. The former procedure constructs an adaptive cosine image by using a cosine function based on an object structure and then individually implants it into each object detectable. This stage provides the first significant property, spatial redistribution, to the object implanted. The adaptive cosine image redistributes the objects for controllability. Then, the latter procedure transforms a complete multiple object-implanted image into the frequency domain. At this point, the second significant property, frequency redistribution, provides the simple technique for identifying and separating the target object from the unwanted objects and background. In this paper, these properties are referred to as spatial-frequency redistribution. This method was realized as a computer program, and then such program was tested with a psychological pattern dataset and a Thai text dataset. The experimental results, when compared with state-of-the-art methods, show that the proposed SFR method can achieve the clear detection of the attention region in the psychological pattern dataset. Moreover, the proposed method can achieve a F-value of 80.28% with a recall and precision of 76.32% and 85.23%, respectively, for Thai text localisation in the Thai text dataset.

Titijaroonroj, Taravichet

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