###### Abstract  
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%). Keywords:liver biopsies; fatty liver; hepatocyte ballooning; deep learning; convolutional neural networks; computer vision +
Footnote †: journal:NAFLD, with histology being the gold standard in modern clinical trials. In this case, the microscopy method on biopsy needle samples makes it possible for all anatomical liver tissue structures, including those of NAFLD and NASH to be examined. In the context of NAFLD, steatosis is predominantly macrovesicular with single and large lipid intracytoplasmic vacuoles pushing aside the hepatocellular nuclei [2]. Occasionally, microvesicular steatosis with multiple small vacuoles within the cytoplasm can be observed, as well as large areas of macrovesicular steatosis agglomeration. In contrast, ballooned hepatocytes present enlarged round cells surrounded by a clear and vacuolar cytoplasm. Even though liver biopsy is considered the gold standard for evaluating NAFLD and NASH activity, it is an invasive patient procedure [3]. In recent decades, many studies have relied on semi-quantitative predictions for chronic and end-stage liver diseases, which lack diagnostic accuracy due to diagnostic obstacles such as \"inter-observer\" and \"intra-observer\" variability. Each case involved subjective microscopic interpretations that came from specialized hepatologists [4]. According to Figure 1, the visual counting of these tissue alterations suggests a difficult and time-consuming process. To overcome this obstacle, modern studies have focused on the development of automated examinations using digital image processing techniques, which can effectively diagnose NAFLD and NASH [5]. A significant number of research efforts focus on the quantification of liver steatosis. These approaches utilize a combination of image processing techniques (including regions of interest segmentation) with supervised machine learning techniques, using manually annotated features. The risk of hepatic obstruction, which refers to the blockage of the bile ducts, sinusoids, portal veins, etc., has led researchers to develop fat detection systems [6; 7; 8; 9], combined with trained classifiers for the separation of fat tissue from other histological structures [10; 11]. Thanks to the effectiveness of these diagnostic systems, histopathology has focused on more complex identification problems, including hepatocellular ballooning and tissue inflammation. These are two chronic diseases for which no automated diagnostic solutions existed until recently [12]. The field of histopathology needed a new generation of algorithms with more independent approaches to the segmentation and classification problems.