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.
Abstract (translated)
根据不同的研究，墨西哥的皮肤病识别是一个重要的问题。文献中的许多工作使用了不同数据仓库的数据集，但没有进行数据分析行为的研究，尤其是在医学图像领域。在这项工作中，我们提出了一种预处理dermaMNIST数据集的方法，以提高其在分类阶段的质量，我们在这一阶段采用了轻量级卷积神经网络。通过我们的方法，在减少用于神经网络训练的样本数量的同时，我们获得的模型性能与ResNet等其他模型相当。
URL
https://arxiv.org/abs/2504.01208
