4.6 Article

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

期刊

APPLIED SCIENCES-BASEL
卷 10, 期 1, 页码 -

出版社

MDPI
DOI: 10.3390/app10010042

关键词

liver biopsies; fatty liver; hepatocyte ballooning; deep learning; convolutional neural networks; computer vision

资金

  1. European Union [5033187]
  2. Greek national funds through the Operational Program for Research and Innovation Smart Specialization Strategy (RIS3) of Ipeiros [5033187]

向作者/读者索取更多资源

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%).

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