通过实例筛选与通道优化，提升皮肤疾病识别模型效率
Lightweight Deep Models for Dermatological Disease Detection: A Study on Instance Selection and Channel Optimization
- 针对dermaMNIST数据集设计预处理方法，筛选有效样本
- 仅用少量样本训练轻量模型，性能接近ResNet
- 适合资源受限场景下的医学图像分类应用
墨西哥多项研究指出，皮肤疾病识别是重要问题。现有文献多直接使用公开数据集，未深入分析医疗图像数据特性。本文提出一种针对dermaMNIST数据集的预处理方法，旨在提升其质量以优化分类性能，采用轻量级卷积神经网络进行训练。实验结果显示，通过减少训练样本数量，模型性能仍可达到ResNet水平，显著降低计算开销。
原文摘要 · Abstract (English)
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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