https://www.selleckchem.com/products/vt103.html
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 Leve
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