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Radiomics for Gleason Score Detection through Deep Learning

Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy
Institute for Informatics and Telematics, National Research Council of Italy, 56121 Pisa, Italy
Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80100 Napoli, Italy
Author to whom correspondence should be addressed.
Sensors 2020, 20(18), 5411;
Received: 1 September 2020 / Accepted: 18 September 2020 / Published: 21 September 2020
(This article belongs to the Special Issue Deep Learning for Cancer Detection)
Prostate cancer is classified into different stages, each stage is related to a different Gleason score. The labeling of a diagnosed prostate cancer is a task usually performed by radiologists. In this paper we propose a deep architecture, based on several convolutional layers, aimed to automatically assign the Gleason score to Magnetic Resonance Imaging (MRI) under analysis. We exploit a set of 71 radiomic features belonging to five categories: First Order, Shape, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix and Gray Level Size Zone Matrix. The radiomic features are gathered directly from segmented MRIs using two free-available dataset for research purpose obtained from different institutions. The results, obtained in terms of accuracy, are promising: they are ranging between 0.96 and 0.98 for Gleason score prediction. View Full-Text
Keywords: prostate; cancer; radiomic; deep learning prostate; cancer; radiomic; deep learning
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MDPI and ACS Style

Brunese, L.; Mercaldo, F.; Reginelli, A.; Santone, A. Radiomics for Gleason Score Detection through Deep Learning. Sensors 2020, 20, 5411.

AMA Style

Brunese L, Mercaldo F, Reginelli A, Santone A. Radiomics for Gleason Score Detection through Deep Learning. Sensors. 2020; 20(18):5411.

Chicago/Turabian Style

Brunese, Luca, Francesco Mercaldo, Alfonso Reginelli, and Antonella Santone. 2020. "Radiomics for Gleason Score Detection through Deep Learning" Sensors 20, no. 18: 5411.

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