Research Article
Automated Classification of Prostate Cancer Severity Using Pre-trained Models
@INPROCEEDINGS{10.1007/978-3-031-60665-6_35, author={S\^{\i}lvia Barros and Vitor Filipe and Lio Gon\`{e}alves}, title={Automated Classification of Prostate Cancer Severity Using Pre-trained Models}, proceedings={Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings}, proceedings_a={MOBIHEALTH}, year={2024}, month={6}, keywords={ISUP Grading Gleason Score Resnet-50 VGG19 InceptionV3}, doi={10.1007/978-3-031-60665-6_35} }- Sílvia Barros
Vitor Filipe
Lio Gonçalves
Year: 2024
Automated Classification of Prostate Cancer Severity Using Pre-trained Models
MOBIHEALTH
Springer
DOI: 10.1007/978-3-031-60665-6_35
Abstract
Prostate cancer is one of the most common types of cancer in men. The ISUP grade and Gleason Score are terms related to the classification of this cancer based on the histological characteristics of the tissues examined in a biopsy. This paper explains an approach that utilizes and evaluates pre-trained models such as ResNet-50, VGG19, and InceptionV3, regarding their ability to automatically classify prostate cancer and its severity based on images and masks annotated with ISUP grades and Gleason Scores. At the end of the training, the performance of each trained model is presented, as well as the comparison between the original and predicted data. This comparison aims to understand if this approach can indeed be used for a more automated classification of prostate cancer.
