An experimental study on classification of thyroid histopathology images using transfer learning

CAD systems for histopathology image analysis using machine learning is a well researched subject. Deep learning is playing a major role in advancing this research in the recent years. This paper presents an automated thyroid histopathology image classification system with deep neural networks using the theory of transfer learning and popular pre-trained convolutional neural networks (CNNs). In this experiment based study, two forms of transfer learning namely feature extraction and fine tuning are applied on popular state-of-the-art CNN architectures such as VGGNet, ResNet, InceptionNet and DenseNet to classify thyroid histopathology images. Accuracy, precision, sensitivity, specificity, area under receiver operating characteristic (AUROC) analysis and F1-score are used to evaluate the performance of the architectures. The results are promising and demonstrate the feasibility of transfer learning for thyroid histopathology image analysis. (C) 2020 Elsevier B.V. All rights reserved.