Abstract
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.
Abstract (translated)
皮肤癌可以通过皮肤镜检查和肉眼观察来识别，但早期检测可以显著提高生存率。人工智能（AI）通过使用标注的皮肤图像和卷积神经网络（CNNs），提高了诊断准确性。本文提出了一种利用软投票集成卷积神经网络进行早期皮肤癌分类的方法。在本研究中，使用了三个基准数据集，分别是HAM10000、ISIC 2016 和 ISIC 2019。该过程包括重新平衡、图像增强和过滤技术，随后通过迁移学习采用混合双编码器进行分割。准确的分割使分类模型聚焦于临床上重要的特征，减少了背景伪影并提高了准确性。通过MobileNetV2、VGG19 和 InceptionV3 的集成进行分类，在保证精度的同时也考虑了速度，以适应实际部署需求。该方法在三个数据集上分别实现了0.9632、0.9086和0.9392的病灶识别准确率。系统性能通过既定的皮肤病变检测指标进行了评估，取得了令人印象深刻的结果。
URL
https://arxiv.org/abs/2512.20431
