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Data Augmentation for Improved Melanoma Classification in Convolutional Neural Networks

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Advanced Research in Technologies, Information, Innovation and Sustainability (ARTIIS 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2346))

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Abstract

Convolutional Neural Networks (CNNs) are highly effective in various computer vision tasks. However, their performance is heavily dependent on the quality and quantity of the training data. This study aims to optimise CNN performance using pattern augmentation techniques to address the problem of limited training data in medical image analysis. Using the DenseNet architecture and the HAM10000 dataset of melanoma and non-melanoma images, the researchers applied a range of augmentation methods, including random rotation, brightness adjustment, random zoom, horizontal flip, height and width shift, and random cropping. The methodology involved evaluating the impact of these augmentation techniques on CNN training. The results showed that random crop and horizontal flip significantly improved classification accuracy by 1–2% over 30 epochs. Extending training to 50 epochs further enhanced performance, though not all methods showed significant improvements; random crop and horizontal flip remained the most effective. These findings highlight the crucial role of selecting appropriate augmentation techniques and training epochs in optimizing CNN performance. The study underscores the importance of dataset diversity and augmentation strategies, particularly in medical image analysis where early and accurate diagnosis is critical. Future research should explore different network architectures and datasets to validate and generalize these results.

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Acknowledgments

The authors extend their appreciation to the Doctorate Program in Intelligent Industry at the Pontifical Catholic University of Valparaiso for supporting this work.

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Authors and Affiliations

  1. Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

    Pamela Hermosilla, Ricardo Soto, Jefté Ponce, Cristian Suazo Jara, Broderick Crawford & Sebastián Contreras

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  1. Pamela Hermosilla
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  2. Ricardo Soto
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  4. Cristian Suazo Jara
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Correspondence to Pamela Hermosilla .

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  1. Universidad Estatal Península de Santa Elena, Santa Elena, Ecuador

    Teresa Guarda

  2. University of Minho, Guimarães, Portugal

    Filipe Portela

  3. Universidad Andrés Bello, Santiago, Chile

    Gustavo Gatica

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Hermosilla, P., Soto, R., Ponce, J., Suazo Jara, C., Crawford, B., Contreras, S. (2025). Data Augmentation for Improved Melanoma Classification in Convolutional Neural Networks. In: Guarda, T., Portela, F., Gatica, G. (eds) Advanced Research in Technologies, Information, Innovation and Sustainability. ARTIIS 2024. Communications in Computer and Information Science, vol 2346. Springer, Cham. https://doi.org/10.1007/978-3-031-83210-9_13

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  • DOI: https://doi.org/10.1007/978-3-031-83210-9_13

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