Breast cancer is one of the most critical and aggressive pathologies suffered in the majority of women in the world, women in Peru are not free to suffer from this pathology, this paper presents a technique for detection of the malignant and benign microcalcifications, using digital mammography images, for the training and validation stage the use of a database containing images corresponding to microcalcifications classified as benign and malignant was used, these images of the database were created From mammographic images containing microcalcifications, these images correspond to Peruvian patients, the Python programming language was used with the TensorFlow and Keras library, with the use of them a deep learnig network was designed with which results were obtained that give a high probability of being used in the clinical environment, these results are the order of 0.94. This article presents as a proposed methodology the design of the database of the images used for the training of the deep learning network as well as the structure of the network.
|Original language||American English|
|Title of host publication||2019 7th E-Health and Bioengineering Conference, EHB 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Indexed - Nov 2019|
|Event||7th IEEE International Conference on E-Health and Bioengineering, EHB 2019 - Iasi, Romania|
Duration: 21 Nov 2019 → 23 Nov 2019
|Name||2019 7th E-Health and Bioengineering Conference, EHB 2019|
|Conference||7th IEEE International Conference on E-Health and Bioengineering, EHB 2019|
|Period||21/11/19 → 23/11/19|
Bibliographical notePublisher Copyright:
© 2019 IEEE.
- Benign malignant
- Deep learning