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Contrastive Learning Embedded Siamese Neural Network for the Assessment of Fatty Liver

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dc.contributor.authorMohit, Kumar
dc.contributor.authorShukla, Ankit
dc.contributor.authorGupta, Rajeev
dc.contributor.authorSingh, Pramod Kumar
dc.contributor.authorAgarwal, Kushagra
dc.contributor.authorKumar, Basant
dc.date.accessioned2025-01-28T10:01:11Z
dc.date.available2025-01-28T10:01:11Z
dc.date.issued2023
dc.description.abstractThis paper presents an self-supervised Siamese neural network (SNN) for identification and classification of fatty liver severity. SNN is used for self-supervision tasks for being influenced from model optimization property of supervised and manual annotation property of unsupervised learning. This technique is based on contrastive learning of the joint embedding network which can learn more subtle representations from the medical images for classification task, with just one or few number of labelled images required from each class for training. The efficiency of the proposed model is validated on our dataset of liver ultrasound to classify them into three stages of the fatty liver disease and normal liver. A two-class classifier (normal/grade-I, normal/grade-II and normal/grade-III fatty liver) and four-class classifier (normal, grade-I, grade-II, grade-III fatty liver disease) were trained by minimizing contrastive loss to obtain classification accuracy of 98.91% and 96.84% respectively. � 2023 IEEE.
dc.identifier.doihttps://doi.org/10.1109/TENCON58879.2023.10322413
dc.identifier.isbn979-835030219-6
dc.identifier.issn21593442
dc.identifier.urihttps://dl.bhu.ac.in/ir/handle/123456789/23219
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectContrastive learning
dc.subjectDeep learning
dc.subjectFatty liver
dc.subjectResNet
dc.subjectSiamese neural network
dc.subjectUltrasound images
dc.titleContrastive Learning Embedded Siamese Neural Network for the Assessment of Fatty Liver
dc.typeArticle
dspace.entity.typePublication
journal.titleIEEE Region 10 Annual International Conference, Proceedings/TENCON

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