A comparative study on the implementation of deep learning algorithms for detection of hepatic necrosis in toxicity studies.

ABSTRACT: Deep learning has recently become one of the most popular methods of image analysis. In non-clinical studies, several tissue slides are generated to investigate the toxicity of a test compound. These are converted into digital image data using a slide scanner, which is then studied by researchers to investigate abnormalities, and the deep learning method has been started to adopt in this study. However, comparative studies evaluating different deep learning algorithms for analyzing abnormal lesions are scarce. In this study, we applied three algorithms, SSD, Mask R-CNN, and DeepLabV3⁺, to detect hepatic necrosis in slide images and determine the best deep learning algorithm for analyzing abnormal lesions. We trained each algorithm on 5750 images and 5835 annotations of hepatic n

SUBMITTER: Hwang JH 

PROVIDER: S-EPMC10313597 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature