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Open AccessArticle

Training of Deep Convolutional Neural Networks to Identify Critical Liver Alterations in Histopathology Image Samples

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Department of Informatics and Telecommunications, University of Ioannina, GR47100 Arta, Greece
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Department of Electrical and Computer Engineering, University of Western Macedonia, GR50100 Kozani, Greece
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Liver Unit/ Division of Integrative Systems Medicine and Digestive Disease, Department of Surgery and Cancer, Imperial College, London SW7 2AZ, UK
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(1), 42; https://doi.org/10.3390/app10010042
Received: 13 October 2019 / Revised: 5 December 2019 / Accepted: 12 December 2019 / Published: 19 December 2019
Nonalcoholic fatty liver disease (NAFLD) is responsible for a wide range of pathological disorders. It is characterized by the prevalence of steatosis, which results in excessive accumulation of triglyceride in the liver tissue. At high rates, it can lead to a partial or total occlusion of the organ. In contrast, nonalcoholic steatohepatitis (NASH) is a progressive form of NAFLD, with the inclusion of hepatocellular injury and inflammation histological diseases. Since there is no approved pharmacotherapeutic solution for both conditions, physicians and engineers are constantly in search for fast and accurate diagnostic methods. The proposed work introduces a fully automated classification approach, taking into consideration the high discrimination capability of four histological tissue alterations. The proposed work utilizes a deep supervised learning method, with a convolutional neural network (CNN) architecture achieving a classification accuracy of 95%. The classification capability of the new CNN model is compared with a pre-trained AlexNet model, a visual geometry group (VGG)-16 deep architecture and a conventional multilayer perceptron (MLP) artificial neural network. The results show that the constructed model can achieve better classification accuracy than VGG-16 (94%) and MLP (90.3%), while AlexNet emerges as the most efficient classifier (97%). View Full-Text
Keywords: liver biopsies; fatty liver; hepatocyte ballooning; deep learning; convolutional neural networks; computer vision liver biopsies; fatty liver; hepatocyte ballooning; deep learning; convolutional neural networks; computer vision
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Arjmand, A.; Angelis, C.T.; Christou, V.; Tzallas, A.T.; Tsipouras, M.G.; Glavas, E.; Forlano, R.; Manousou, P.; Giannakeas, N. Training of Deep Convolutional Neural Networks to Identify Critical Liver Alterations in Histopathology Image Samples. Appl. Sci. 2020, 10, 42.

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