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Automated Malignant Melanoma Classification Using Convolutional Neural Networks

Ciencia e ingeniería neogranadina, 2022-12, Vol.32 (2), p.171-185 [Peer Reviewed Journal]

2022. This work is published under https://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. ;ISSN: 0124-8170 ;ISSN: 1909-7735 ;EISSN: 1909-7735 ;DOI: 10.18359/rcin.6270

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  • Title:
    Automated Malignant Melanoma Classification Using Convolutional Neural Networks
  • Author: Guillermo Guarnizo, José ; Riaño Borda, Sebastián ; Camacho Poveda, Edgar Camilo ; Mateus Rojas, Armando
  • Subjects: Artificial intelligence ; Artificial neural networks ; Classification ; Classifiers ; Computer vision ; Convolution ; Convolutional Neural Networks ; Dermoscopy ; Diagnosis ; Digital imaging ; ENGINEERING, MULTIDISCIPLINARY ; Machine learning ; Medical imaging ; Melanoma Detection ; Neural networks ; Pattern recognition ; Skin cancer
  • Is Part Of: Ciencia e ingeniería neogranadina, 2022-12, Vol.32 (2), p.171-185
  • Description: This research is proposed a design of architecture for melanoma (a kind of skin cancer) recognition by using a Convolutional Neural Network (CNN), work that will be useful for researchers in future projects in areas like biomedicine, machine learning, and others related moving forward with their studies and improving this proposal. CNN is mostly used in computer vision (a branch of artificial intelligence), applied to pattern recognition in skin moles and to determine the existence of malignant melanoma, or not, with a limited dataset. The CNN classifier designed and trained in this case was built through a couple of layers of convolution and pooling stacked to form a neural network of 6 layers followed by the fully connected to complete the architecture with an output classifier. The proposed database to train our CNN is the largest publicly collection of dermoscopic images of melanomas and other skin lesions, provided by the International Skin Imaging Collaboration (ISIC), sponsored by International Society for Digital Imaging of the Skin (ISDIS), an international effort to improve melanoma diagnosis. The purpose of this research was to design a Convolutional Neural Network with a high level of accuracy to help professionals in medicine with a melanoma diagnosis, in this case, it was possible to get accuracy up to 88.75 %.
  • Publisher: Bogotá: Universidad Militar Nueva Granada
  • Language: English;Portuguese
  • Identifier: ISSN: 0124-8170
    ISSN: 1909-7735
    EISSN: 1909-7735
    DOI: 10.18359/rcin.6270
  • Source: SciELO
    ProQuest Central
    DOAJ Directory of Open Access Journals
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