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- Title
Radiomics for Gleason Score Detection through Deep Learning.
- Authors
Brunese, Luca; Mercaldo, Francesco; Reginelli, Alfonso; Santone, Antonella
- Abstract
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.
- Subjects
GLEASON grading system; MAGNETIC resonance imaging; DEEP learning; FORECASTING; PROSTATE cancer; ZONE melting
- Publication
Sensors (14248220), 2020, Vol 20, Issue 18, p5411
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s20185411