Automated Gleason grading of prostate cancer using transfer learning from general-purpose deep-learning networks

Mircea-Sebastian Şerbănescu, Department of Medical Informatics and Biostatistics, University of Medicine and Pharmacy of Craiova, Romania, Nicolae Cătălin Manea, Liliana Streba, Smaranda Belciug, Iancu Emil Pleşea, Ionica Pirici, Raluca Maria Bungărdean, Răzvan Mihail Pleşea, Department of Medical Informatics and Biostatistics, University of Medicine and Pharmacy of Craiova, Romania, Department of Medical Oncology, University of Medicine and Pharmacy of Craiova, Romania, Department of Computer Science, University of Craiova, Romania (+4 others)
2020 Romanian journal of morphology and embryology  
Two deep-learning algorithms designed to classify images according to the Gleason grading system that used transfer learning from two well-known general-purpose image classification networks (AlexNet and GoogleNet) were trained on Hematoxylin-Eosin histopathology stained microscopy images with prostate cancer. The dataset consisted of 439 images asymmetrically distributed in four Gleason grading groups. Mean and standard deviation accuracy for AlexNet derivate network was of 61.17±7 and for
more » ... leNet derivate network was of 60.9±7.4. The similar results obtained by the two networks with very different architecture, together with the normal distribution of classification error for both algorithms show that we have reached a maximum classification rate on this dataset. Taking into consideration all the constraints, we conclude that the resulted networks could assist pathologists in this field, providing first or second opinions on Gleason grading, thus presenting an objective opinion in a grading system which has showed in time a great deal of interobserver variability.
doi:10.47162/rjme.61.1.17 fatcat:vmea5anszncsthkhs7djg5khoi