Artificial intelligence applied to breast cancer classification Conference Paper
| Authors: | Acosta-Jiménez, S.; Camarillo-Cisneros, J.; Guzmán-Pando, A.; González-Chávez, S. A.; Galván-Tejada, J. I.; Ramírez-Alonso, G.; Pacheco-Tena, C. F.; Ochoa-Albiztegui, R. E. |
| Title: | Artificial intelligence applied to breast cancer classification |
| Conference Title: | Congreso Nacional de Ingeniería Biomédica (CNIB) 2022 |
| Abstract: | One in eight women is likely to develop breast cancer at some stage in her life, with a 12.5% average risk rate of developing breast cancer. Early detection and treatment are of vital importance to ensure the patient's survival. Currently, mammography is the main diagnostic study to identify breast cancer. However, since mammography requires a human, medical radiologist, to make a diagnosis, it is prone to errors. Recently, deep learning techniques have proven to be a suitable tool for breast cancer classification and detection. Therefore, this research proposes an algorithm based on convolutional neural networks (CNN) for screening classification of cancer in mammography images. The evaluation results of the proposed algorithm respect state-of-the-art algorithms demonstrate competitive accuracy results of up to 99% and the fastest training time. Therefore, our algorithm is well suitable for automatic breast cancer detection using the public All-MIAS database. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
| Keywords: | breast cancer; mammography; patient treatment; diseases; breast-cancer; deep learning; convolutional neural network; convolutional neural networks; average risk; patient survivals; ai; learning techniques; breast cancer classifications; breast cancer detection; evaluation results; mammography images; risk rate |
| Journal Title | IFMBE Proceedings |
| Volume: | 86 |
| Conference Dates: | 2022 Oct 6-8 |
| Conference Location: | Puerto Vallarta, México |
| ISBN: | 1680-0737 |
| Publisher: | Springer |
| Date Published: | 2023-01-01 |
| Start Page: | 83 |
| End Page: | 93 |
| Language: | English |
| DOI: | 10.1007/978-3-031-18256-3_8 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Conference Paper -- Export Date: 3 January 2023 -- Source: Scopus |
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