Identificación automática de cáncer de piel aplicando machine learning

Anderson Smith Florez Fuentes; Rafael Guzmán Cabrera; Everardo Vargas Rodriguez

doi:10.24054/rcta.v2i40.2350

Authors

Anderson Smith Florez Fuentes Universidad de Guanajuato https://orcid.org/0000-0001-6176-1071
Rafael Guzmán Cabrera Universidad de Guanajuato https://orcid.org/0000-0002-9320-7021
Everardo Vargas Rodriguez Universidad de Guanajuato https://orcid.org/0000-0001-5480-3384

DOI:

https://doi.org/10.24054/rcta.v2i40.2350

Keywords:

Skin cancer, Machine learning, processing, data

Abstract

Currently the implementation of machine learning in medicine, especially in oncology, has become a key tool for the detection of malformations, infections, heart problems, cancer, among others, anomalies of the human body that by applying machine learning, these processes become tools that strengthen the medical diagnosis. This work presents a literature review and the implementation with Python to analyze the behavior of the implemented data, performing an execution for four types of models such as support vector machine, k-nearest neighbor, Neural Network and Decision Tree, these models are applied to the HAM10000 data, which contains manually labeled medical images, which ensures that the delivery process is effective. In the implementation of a balanced sample, which allows to demonstrate the efficiency of the implementation of machine learning in the identification of skin cancer, this implementation has a great variability in the results, reaching values up to 100% accuracy in the implementation.

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