Convolutional Neural Network Model for Basal Cell Carcinoma Detection

Authors

  • Rolando Jesus Zafra Moran Universidad Nacional Mayor de San Marcos, Facultad de Ingeniería de Sistemas e Informática, Lima, Peru https://orcid.org/0009-0000-8700-1775
  • Edgar Fernando López Loaiza Universidad Nacional Mayor de San Marcos, Facultad de Ingeniería de Sistemas e Informática, Lima, Peru https://orcid.org/0009-0005-6073-2769
  • Nicole Gabriela Tumi Alarcón Universidad Nacional Mayor de San Marcos, Facultad de Ingeniería de Sistemas e Informática, Lima, Peru https://orcid.org/0009-0005-1105-8392
  • Pedro Martin Lezama Gonzales Universidad Nacional Mayor de San Marcos, Facultad de Ingeniería de Sistemas e Informática, Lima, Peru

DOI:

https://doi.org/10.15381/risi.v16i2.25773

Keywords:

Neural networks, Basal cell carcinoma, machine learning

Abstract

Basal cell carcinoma (BCC) is the most common type of skin cancer, accounting for approximately 80% of all cases. Early and accurate detection of BCC is essential for effective treatment and prevention of serious complications. In this work, a CNN (Convolutional Neural Networks) model is presented for the detection and classification of Basal Cell Carcinoma from dermoscopic cases. To create the proposed model, the HAM10000 dataset was used, which includes a set of images of dermatological lesions. Extensive experiments were performed to evaluate the accuracy of the model, as well as a comparison with the GRU and LSTM models. The results obtained indicate an accuracy of 93.5%, demonstrating that the proposed model has the ability to effectively identify and differentiate benign and malignant basal cell carcinoma lesions.

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Published

2023-12-30

Issue

Vol. 16 No. 2 (2023)

Section

Original Research Articles

License

Copyright (c) 2023 Rolando Jesus Zafra Moran, Nicole Gabriela Tumi Alarcón, Edgar Fernando López Loaiza, Pedro Martin Lezama Gonzales

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

AUTHORS RETAIN THEIR RIGHTS:

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c. Authors retain theirs right to make a subsequent publication of their work, to use the article or any part thereof (eg a compilation of his papers, lecture notes, thesis, or a book), always indicating its initial publication in the Revista de investigación de Sistemas e Informática (the originator of the work, journal, volume, number and date).

How to Cite

[1]
“Convolutional Neural Network Model for Basal Cell Carcinoma Detection”, Rev.Investig.sist.inform., vol. 16, no. 2, pp. 33–41, Dec. 2023, doi: 10.15381/risi.v16i2.25773.