Titijaroonroj, Taravichet

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Titijaroonroj, Taravichet
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Titijaroonrog, Taravichet
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taravichet.ti@kmitl.ac.th

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    Item type:Publication,
    Automatic Lymph Node Classification with Convolutional Neural Network
    (2022-01-01)
    Uthatham, Ason
    ;
    Yodrabum, Nutcha
    ;
    Sinmaroeng, Chanya
    ;
    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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    LymphoNet: A Deep Learning for Lymph Node Detection from Histological Image
    (2024-01-01)
    Uthatham, Ason
    ;
    Yodrabum, Nutcha
    ;
    Sinmaroeng, Chanya
    ;
    Chaikangwan, Irin
    ;
    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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    Deep learning-based classification of lymphedema and other lower limb edema diseases using clinical images
    (2025-12-01)
    Lewsirirat, Thanat
    ;
    Titijaroonroj, Taravichet
    ;
    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.