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In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to bedoi:10.3390/jpm10040224 pmid:33198332 pmcid:PMC7711876 fatcat:lwfgkwj5trd3beaodeucuzd5ou