Presentation
Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks
Author/s | Durán López, Lourdes
Domínguez Morales, Juan Pedro Amaya Rodríguez, Isabel Luna Perejón, Francisco Civit Masot, Javier Vicente Díaz, Saturnino Linares Barranco, Alejandro |
Department | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores |
Publication Date | 2019 |
Deposit Date | 2020-02-12 |
Published in |
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ISBN/ISSN | 978-989-758-384-1 |
Abstract | Breast cancer is one of the most frequent causes of mortality in women. For the early detection of breast cancer,
the mammography is used as the most efficient technique to identify abnormalities such as tumors. ... Breast cancer is one of the most frequent causes of mortality in women. For the early detection of breast cancer, the mammography is used as the most efficient technique to identify abnormalities such as tumors. Automatic detection of tumors in mammograms has become a big challenge and can play a crucial role to assist doctors in order to achieve an accurate diagnosis. State-of-the-art Deep Learning algorithms such as Faster Regional Convolutional Neural Networks are able to determine the presence of an object and also its position inside the image in a reduced computation time. In this work, we evaluate these algorithms to detect tumors in mammogram images and propose a detection system that contains: (1) a preprocessing step performed on mammograms taken from the Digital Database for Screening Mammography (DDSM) and (2) the Neural Network model, which performs feature extraction over the mammograms in order to locate tumors within each image and classify them as malignant or benign. The results obtained show that the proposed algorithm has an accuracy of 97.375%. These results show that the system could be very useful for aiding physicians when detecting tumors from mammogram images. |
Project ID. | TEC2016-77785-P |
Citation | Durán López, L., Domínguez Morales, J.P., Amaya Rodríguez, I., Luna Perejón, F., Civit Masot, J., Vicente Díaz, S. y Linares Barranco, A. (2019). Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks. En IJCCI 2019: 11th International Joint Conference on Computational Intelligence (444-448), Vienna, Austria: ScitePress Digital Library. |
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Breast Cancer Automatic Diagno ... | 1.199Mb | [PDF] | View/ | |