Identifying gross post‐mortem organ images using a pre‐trained convolutional neural network. Issue 2 (26th October 2020) - Garland, Jack; Hu, Mindy; Kesha, Kilak; Glenn, Charley; Morrow, Paul; Stables, Simon; Ondruschka, Benjamin; Tse, Rexson
Abstract: Identifying organs/tissue and pathology on radiological and microscopic images can be performed using convolutional neural networks (CNN). However, there are scant studies on applying CNN to post‐mortem gross images of visceral organs. This proof‐of‐concept study used 537 gross post‐mortem images of dissected brain, heart, lung, liver, spleen, and kidney, which were randomly divided into a training and teaching datasets for the pre‐trained CNN Xception. The CNN was trained using the training dataset and subsequently tested on the testing dataset. The overall accuracies were >95% percent for both training and testing datasets and have an F1 score of >0.95 for all dissected organs. This study showed that small datasets of post‐mortem images can be classified with a very high accuracy using a pre‐trained CNN. This novel area has the potential for future application in data mining, education and teaching, case review, research, quality assurance, auditing purposes, and identifying pathology.