Contrastive Learned Self-Supervised Technique for Fatty Liver and Chronic Liver Identification

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PurposeLiver diseases are life-threatening problems when not cured in time. The root of the prevention lies in the early identification of fatty liver diseases, which may be one of the primary causes of most chronic liver diseases. Computer-aided diagnosis (CAD) through deep learning techniques proved to be a success in the medical field for early detections when implementing progressive algorithms. MethodsThis paper proposes a self-supervised Siamese neural network (SNN) for fatty liver identification. SNN is influenced by the model optimization property of unsupervised and manual annotation property of supervised learning. This technique is based on contrastive learning of the joint embedding network, which can learn more subtle representations from the unlabelled medical images for the classification task, with just one or few labelled images required from each class for training. Detailed appearance differences between two images are contrasted during training for learning the representations. It makes use of feedback supervisory signals across the dataset to learn the data structure itself without relying on labelling whole data. ResultsThe efficiency of the proposed model has been validated on liver ultrasound images to classify them into normal, fatty liver grade-I (mild), II (moderate), III (severe), and chronic liver diseases in a two-class classifier (normal/abnormal) and five-class classifier by minimizing contrastive loss to obtain best classification accuracy of 99.90% and 99.77% respectively. ConclusionOverall experimental results achieved from the proposed model proved reliable enough to assist medical specialists in screening of liver diseases.