2nd International Conference on Computing and Machine Intelligence, ICMI 2022, İstanbul, Turkey, 15 - 16 July 2022
© 2022 IEEE.Steatosis quantification is an essential issue for accurate diagnosis and donor transplantation. However, manually quantification processes of steatosis by a pathologist have some difficulties because of time-consuming and tiring processes that can vary in inter and intra-experts. In recent years, deep learning studies have emerged with promising performance on steatosis quantification. On the other hand, deep learning models require a large amount of data, yet the steatosis dataset is insufficient for deep models. Thus, we propose deep learning model consisting of two steps that showed high performance even on a small number of steatosis datasets. The first step is unsupervised feature extraction with UNet. The second step is classification by using extracted features as an input for classification models. ResNet-50, EfficientNet B1 and MobileNetV2 networks are used for classification. As a result, the proposed deep models enable fully automated steatosis quantification with high AUC.