Breast Cancer Detection by Means of Artificial Neural Networks
| dc.creator | Ortiz Rodriguez, Jose Manuel | |
| dc.creator | Guerrero Mendez, Carlos | |
| dc.creator | Martinez Blanco, Maria del Rosario | |
| dc.creator | Castro Tapia, Salvador | |
| dc.creator | Moreno Lucio, Mireya | |
| dc.creator | Jaramillo Martinez, Ramon | |
| dc.creator | Solis Sanchez, Luis Octavio | |
| dc.creator | Martinez Fierro, Margarita de la Luz | |
| dc.creator | Garza Veloz, Idalia | |
| dc.creator | Cruz Moreira Galvan, Jose | |
| dc.creator | Barrios Garcia, Jorge Alberto | |
| dc.date.accessioned | 2021-01-21T17:59:26Z | |
| dc.date.available | 2021-01-21T17:59:26Z | |
| dc.date.created | 2017-12-20 | |
| dc.description.abstractenglish | Breast cancer is a fatal disease causing high mortality in women. Constant efforts are being made for creating more efficient techniques for early and accurate diagnosis. Classical methods require oncologists to examine the breast lesions for detection and classification of various stages of cancer. Such manual attempts are time consuming and inefficient in many cases. Hence, there is a need for efficient methods that diagnoses the cancerous cells without human involvement with high accuracies. In this research, image processing techniques were used to develop imaging biomarkers through mammography analysis and based on artificial intelligence technology aiming to detect breast cancer in early stages to support diagnosis and prioritization of high-risk patients. For automatic classification of breast cancer on mammograms, a generalized regression artificial neural network was trained and tested to separate malignant and benign tumors reaching an accuracy of 95.83%. With the biomarker and trained neural net, a computer-aided diagnosis system is being designed. The results obtained show that generalized regression artificial neural network is a promising and robust system for breast cancer detection. The Laboratorio de Innovacion y Desarrollo Tecnologico en Inteligencia Artificial is seeking collaboration with research groups interested in validating the technology being developed. | spa |
| dc.format.extent | 21 páginas | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.identifier.doi | 10.5772/intechopen.71256 | |
| dc.identifier.other | https://www.intechopen.com/books/advanced-applications-for-artificial-neural-networks/breast-cancer-detection-by-means-of-artificial-neural-networks | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12010/16827 | |
| dc.language.iso | eng | spa |
| dc.publisher | IntechOpen | spa |
| dc.relation.references | Jose Manuel Ortiz-Rodriguez, Carlos Guerrero-Mendez, Maria del Rosario Martinez-Blanco, Salvador Castro-Tapia, Mireya Moreno- Lucio, Ramon Jaramillo-Martinez, Luis Octavio Solis-Sanchez, Margarita de la Luz Martinez-Fierro, Idalia Garza-Veloz, Jose Cruz Moreira Galvan and Jorge Alberto Barrios Garcia (December 20th 2017). Breast Cancer Detection by Means of Artificial Neural Networks, Advanced Applications for Artificial Neural Networks, Adel El-Shahat, IntechOpen, DOI: 10.5772/intechopen.71256. | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.creativecommons | https://creativecommons.org/licenses/by-nc/4.0/legalcode | |
| dc.rights.local | Abierto (Texto Completo) | spa |
| dc.subject | Medicina | spa |
| dc.subject.lemb | Cáncer de mama | spa |
| dc.subject.lemb | Redes neuronales artificiales | spa |
| dc.subject.lemb | Procesando imagen digital | spa |
| dc.title | Breast Cancer Detection by Means of Artificial Neural Networks | spa |
| dc.type.coar | http://purl.org/coar/resource_type/c_2f33 | spa |
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