用多个CNN模型投票提升皮肤病变分类准确率
Skin Lesion Classification Using a Soft Voting Ensemble of Convolutional Neural Networks
- 用MobileNetV2、VGG19、InceptionV3三模型加权投票
- 在HAM10000等三个数据集上最高达96.32%准确率
- 适合临床辅助诊断系统，兼顾精度与实时性
皮肤癌可通过皮肤镜检查和目视观察识别，早期发现显著提升生存率。人工智能利用标注的皮肤图像和卷积神经网络（CNN）可提高诊断准确率。本文提出一种基于软投票集成的早期皮肤癌分类方法。研究使用HAM10000、ISIC 2016和ISIC 2019三个基准数据集，通过重平衡、图像增强和过滤技术预处理，采用迁移学习的混合双编码器进行分割。精准分割使分类模型聚焦于临床关键特征，减少背景干扰，提升准确性。分类采用MobileNetV2、VGG19和InceptionV3的集成，兼顾精度与速度，适用于实际部署。该方法在三个数据集上的病灶识别准确率分别为96.32%、90.86%和93.92%，使用标准皮肤病变检测指标评估，表现优异。
原文摘要 · Abstract (English)
Skin cancer can be identified by dermoscopic examination and ocular inspection, but early detection significantly increases survival chances. Artificial intelligence (AI), using annotated skin images and Convolutional Neural Networks (CNNs), improves diagnostic accuracy. This paper presents an early skin cancer classification method using a soft voting ensemble of CNNs. In this investigation, three benchmark datasets, namely HAM10000, ISIC 2016, and ISIC 2019, were used. The process involved rebalancing, image augmentation, and filtering techniques, followed by a hybrid dual encoder for segmentation via transfer learning. Accurate segmentation focused classification models on clinically significant features, reducing background artifacts and improving accuracy. Classification was performed through an ensemble of MobileNetV2, VGG19, and InceptionV3, balancing accuracy and speed for real-world deployment. The method achieved lesion recognition accuracies of 96.32\%, 90.86\%, and 93.92\% for the three datasets. The system performance was evaluated using established skin lesion detection metrics, yielding impressive results.
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