LymphoNet: A Deep Learning for Lymph Node Detection from Histological Image

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dc.contributor.authorUthatham, Ason
dc.contributor.authorYodrabum, Nutcha
dc.contributor.authorSinmaroeng, Chanya
dc.contributor.authorChaikangwan, Irin
dc.contributor.authorTitijaroonroj, Taravichet
dc.date.accessioned2026-08-06T10:43:39Z
dc.date.available2026-08-06T10:43:39Z
dc.date.issued2024-01-01
dc.description.abstractIdentifying 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.
dc.identifier.citationIEEE Access, 12, 160369-160395, 2024
dc.identifier.doi10.1109/ACCESS.2024.3487260
dc.identifier.issn21693536
dc.identifier.other2-s2.0-85208708541
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14997
dc.sourceIEEE Access
dc.subjectArtificial intelligence
dc.subjectdeep learning
dc.subjecthistopathological slides
dc.subjectlymph node detection
dc.subjectlymphedema
dc.subjectobject detection
dc.subjectsubmental lymph nodes
dc.subjectvascularized lymph node flaps
dc.titleLymphoNet: A Deep Learning for Lymph Node Detection from Histological Image
dc.typeArticle

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