Mostrando 4 resultados de: 4
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Deep‐learning‐based computer‐aided systems for breast cancer imaging: A critical review
ReviewAbstract: This paper provides a critical review of the literature on deep learning applications in breast tumoPalabras claves:Breast Cancer, Computer‐aided diagnosis, convolutional neural networks, deep learning, Mammography, UltrasoundAutores:Lakshminarayanan V., Rodríguez‐álvarez M.J., Yuliana Jiménez-GaonaFuentes:scopusDenseNet for Breast Tumor Classification in Mammographic Images
Conference ObjectAbstract: Breast cancer screening is an efficient method to detect breast lesions early. The common screeningPalabras claves:Breast tumor classification, Convolutional neural network, deep learning, DenseNet, Mammography, RoI alignAutores:Castillo P., Espino-Morato H., Lakshminarayanan V., Rodríguez‐álvarez M.J., Yuliana Jiménez-GaonaFuentes:googlescopusPreprocessing fast filters and mass segmentation for mammography images
Conference ObjectAbstract: Digital mammography is a valuable technique for breast cancer detection, because it is safe, noninvaPalabras claves:Breast Cancer, contrast enhancement, Filters, Mammogram, Preprocessing, segmentationAutores:Castillo P., Darwin Patricio Castillo Malla, Jimmy Freire, Lakshminarayanan V., Rodríguez‐álvarez M.J., Yuliana Jiménez-GaonaFuentes:googlescopusMagnetic resonance brain images algorithm to identify demyelinating and ischemic diseases
Conference ObjectAbstract: Brain demyelination lesions occur due to damage of the myelin layer of nerve fibers, this deterioratPalabras claves:Brain disease, Demyelinating, IMAGE PROCESSING, ischemia, MRIAutores:Castillo P., Darwin Patricio Castillo Malla, Luis Alberto Cuenca Macas, Oscar Vivanco-Galván, René J. Samaniego, Rodríguez‐álvarez M.J., Vivanco-Galván O., Yuliana Jiménez-GaonaFuentes:googlescopus