Clasificación de Imágenes Médicas para la Detección del Cáncer de mama mediante Redes Neuronales

Riera Iziga Marcos; Sotelo Hernández Waldir; Campos Vásquez Neicer

doi:10.18687/LEIRD2021.1.1.33

Clasificación de Imágenes Médicas para la Detección del Cáncer de mama mediante Redes Neuronales

Translated title of the contribution: Classification of medical images for breast cancer detection using neural networks

Riera Iziga Marcos
, Sotelo Hernández Waldir
, Campos Vásquez Neicer

Universidad Privada del Norte

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

At present, it is not easy to interpret mammography images and provide a medical diagnosis by a health professional. Despite the professional's experience, anomalies or tumors are not detected 100% quickly. Since it is not easy to interpret. main objective was to perform an image classifier and apply a convolutional neuron. After a rigorous training of the neuron to provide an efficient diagnosis, it is shown that by increasing training the result is more efficient and the error factor is decreased. Only in the second test was 64.3% effective. In the research, no incompatible images with the neural network were presented, so no attributes were lost if parameter adjustments were made. So it is demonstrable that the system has great practical scope because the resources used are easy to reach.

Translated title of the contribution	Classification of medical images for breast cancer detection using neural networks
Original language	Spanish
Title of host publication	1st LACCEI International Multi-Conference on Entrepreneurship, Innovation, and Regional Development
Subtitle of host publication	Ideas to Overcome and Emerge from the Pandemic Crisis, LEIRD 2021 - Conference
Editors	Maria M. Larrondo Petrie, Luis Felipe Zapata Rivera, Catalina Aranzazu-Suescun
ISBN (Electronic)	9789585207196
DOIs	https://doi.org/10.18687/LEIRD2021.1.1.33
State	Published - 2021
Event	1st LACCEI International Multi-Conference on Entrepreneurship, Innovation, and Regional Development: Ideas to Overcome and Emerge from the Pandemic Crisis, LEIRD 2021 - Virtual, Online, Colombia Duration: 9 Dec 2021 → 10 Dec 2021

Publication series

Name	Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
ISSN (Electronic)	2414-6390

Conference

Conference	1st LACCEI International Multi-Conference on Entrepreneurship, Innovation, and Regional Development: Ideas to Overcome and Emerge from the Pandemic Crisis, LEIRD 2021
Country/Territory	Colombia
City	Virtual, Online
Period	9/12/21 → 10/12/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.18687/LEIRD2021.1.1.33

Cite this

Marcos, R. I., Waldir, S. H., & Neicer, C. V. (2021). Clasificación de Imágenes Médicas para la Detección del Cáncer de mama mediante Redes Neuronales. In M. M. Larrondo Petrie, L. F. Zapata Rivera, & C. Aranzazu-Suescun (Eds.), 1st LACCEI International Multi-Conference on Entrepreneurship, Innovation, and Regional Development: Ideas to Overcome and Emerge from the Pandemic Crisis, LEIRD 2021 - Conference (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology). https://doi.org/10.18687/LEIRD2021.1.1.33

Marcos, Riera Iziga ; Waldir, Sotelo Hernández ; Neicer, Campos Vásquez. / Clasificación de Imágenes Médicas para la Detección del Cáncer de mama mediante Redes Neuronales. 1st LACCEI International Multi-Conference on Entrepreneurship, Innovation, and Regional Development: Ideas to Overcome and Emerge from the Pandemic Crisis, LEIRD 2021 - Conference. editor / Maria M. Larrondo Petrie ; Luis Felipe Zapata Rivera ; Catalina Aranzazu-Suescun. 2021. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology).

@inproceedings{b5e196132aa14ababfe5f834b940c166,

title = "Clasificaci{\'o}n de Im{\'a}genes M{\'e}dicas para la Detecci{\'o}n del C{\'a}ncer de mama mediante Redes Neuronales",

abstract = "At present, it is not easy to interpret mammography images and provide a medical diagnosis by a health professional. Despite the professional's experience, anomalies or tumors are not detected 100\% quickly. Since it is not easy to interpret. main objective was to perform an image classifier and apply a convolutional neuron. After a rigorous training of the neuron to provide an efficient diagnosis, it is shown that by increasing training the result is more efficient and the error factor is decreased. Only in the second test was 64.3\% effective. In the research, no incompatible images with the neural network were presented, so no attributes were lost if parameter adjustments were made. So it is demonstrable that the system has great practical scope because the resources used are easy to reach.",

keywords = "deep learning, diagnosis., image classification, mammography",

author = "Marcos, \{Riera Iziga\} and Waldir, \{Sotelo Hern{\'a}ndez\} and Neicer, \{Campos V{\'a}squez\}",

note = "Publisher Copyright: {\textcopyright} 2021 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.; 1st LACCEI International Multi-Conference on Entrepreneurship, Innovation, and Regional Development: Ideas to Overcome and Emerge from the Pandemic Crisis, LEIRD 2021 ; Conference date: 09-12-2021 Through 10-12-2021",

year = "2021",

doi = "10.18687/LEIRD2021.1.1.33",

language = "Espa{\~n}ol",

series = "Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology",

editor = "\{Larrondo Petrie\}, \{Maria M.\} and \{Zapata Rivera\}, \{Luis Felipe\} and Catalina Aranzazu-Suescun",

booktitle = "1st LACCEI International Multi-Conference on Entrepreneurship, Innovation, and Regional Development",

}

Marcos, RI, Waldir, SH & Neicer, CV 2021, Clasificación de Imágenes Médicas para la Detección del Cáncer de mama mediante Redes Neuronales. in MM Larrondo Petrie, LF Zapata Rivera & C Aranzazu-Suescun (eds), 1st LACCEI International Multi-Conference on Entrepreneurship, Innovation, and Regional Development: Ideas to Overcome and Emerge from the Pandemic Crisis, LEIRD 2021 - Conference. Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology, 1st LACCEI International Multi-Conference on Entrepreneurship, Innovation, and Regional Development: Ideas to Overcome and Emerge from the Pandemic Crisis, LEIRD 2021, Virtual, Online, Colombia, 9/12/21. https://doi.org/10.18687/LEIRD2021.1.1.33

Clasificación de Imágenes Médicas para la Detección del Cáncer de mama mediante Redes Neuronales. / Marcos, Riera Iziga; Waldir, Sotelo Hernández; Neicer, Campos Vásquez.
1st LACCEI International Multi-Conference on Entrepreneurship, Innovation, and Regional Development: Ideas to Overcome and Emerge from the Pandemic Crisis, LEIRD 2021 - Conference. ed. / Maria M. Larrondo Petrie; Luis Felipe Zapata Rivera; Catalina Aranzazu-Suescun. 2021. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Clasificación de Imágenes Médicas para la Detección del Cáncer de mama mediante Redes Neuronales

AU - Marcos, Riera Iziga

AU - Waldir, Sotelo Hernández

AU - Neicer, Campos Vásquez

PY - 2021

Y1 - 2021

N2 - At present, it is not easy to interpret mammography images and provide a medical diagnosis by a health professional. Despite the professional's experience, anomalies or tumors are not detected 100% quickly. Since it is not easy to interpret. main objective was to perform an image classifier and apply a convolutional neuron. After a rigorous training of the neuron to provide an efficient diagnosis, it is shown that by increasing training the result is more efficient and the error factor is decreased. Only in the second test was 64.3% effective. In the research, no incompatible images with the neural network were presented, so no attributes were lost if parameter adjustments were made. So it is demonstrable that the system has great practical scope because the resources used are easy to reach.

AB - At present, it is not easy to interpret mammography images and provide a medical diagnosis by a health professional. Despite the professional's experience, anomalies or tumors are not detected 100% quickly. Since it is not easy to interpret. main objective was to perform an image classifier and apply a convolutional neuron. After a rigorous training of the neuron to provide an efficient diagnosis, it is shown that by increasing training the result is more efficient and the error factor is decreased. Only in the second test was 64.3% effective. In the research, no incompatible images with the neural network were presented, so no attributes were lost if parameter adjustments were made. So it is demonstrable that the system has great practical scope because the resources used are easy to reach.

KW - deep learning

KW - diagnosis.

KW - image classification

KW - mammography

UR - https://www.scopus.com/pages/publications/85139375306

U2 - 10.18687/LEIRD2021.1.1.33

DO - 10.18687/LEIRD2021.1.1.33

M3 - Contribución a la conferencia

AN - SCOPUS:85139375306

T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology

BT - 1st LACCEI International Multi-Conference on Entrepreneurship, Innovation, and Regional Development

A2 - Larrondo Petrie, Maria M.

A2 - Zapata Rivera, Luis Felipe

A2 - Aranzazu-Suescun, Catalina

T2 - 1st LACCEI International Multi-Conference on Entrepreneurship, Innovation, and Regional Development: Ideas to Overcome and Emerge from the Pandemic Crisis, LEIRD 2021

Y2 - 9 December 2021 through 10 December 2021

ER -

Marcos RI, Waldir SH, Neicer CV. Clasificación de Imágenes Médicas para la Detección del Cáncer de mama mediante Redes Neuronales. In Larrondo Petrie MM, Zapata Rivera LF, Aranzazu-Suescun C, editors, 1st LACCEI International Multi-Conference on Entrepreneurship, Innovation, and Regional Development: Ideas to Overcome and Emerge from the Pandemic Crisis, LEIRD 2021 - Conference. 2021. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology). doi: 10.18687/LEIRD2021.1.1.33

Clasificación de Imágenes Médicas para la Detección del Cáncer de mama mediante Redes Neuronales

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