Lightweight Deep Models for Dermatological Disease Detection: A Study on Instance Selection and Channel Optimization

Ian Mateos Gonzalez, Estefani Jaramilla Nava, Abraham Sánchez Morales, Jesús García-Ramírez, Ricardo Ramos-Aguilar
4/1/2025
eess.IVcs.AIcs.CV

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Abstract

The identification of dermatological disease is an important problem in Mexico according with different studies. Several works in literature use the datasets of different repositories without applying a study of the data behavior, especially in medical images domain. In this work, we propose a methodology to preprocess dermaMNIST dataset in order to improve its quality for the classification stage, where we use lightweight convolutional neural networks. In our results, we reduce the number of instances for the neural network training obtaining a similar performance of models as ResNet.

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Cite this paper

@article{gonzalez2025lightweight,
  title  = {Lightweight Deep Models for Dermatological Disease Detection: A Study on Instance Selection and Channel Optimization},
  author = {Ian Mateos Gonzalez and Estefani Jaramilla Nava and Abraham Sánchez Morales and Jesús García-Ramírez and Ricardo Ramos-Aguilar},
  year   = {2025},
  eprint = {2504.01208},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.01208v1}
}

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