用深度学习区分皮肤良恶性病变，DenseNet201准确率达93.79%。
Deep Learning for Dermatology: An Innovative Framework for Approaching Precise Skin Cancer Detection
- 对比VGG16与DenseNet201在皮肤病变分类中的表现
- DenseNet201在3297张图像上达到93.79%准确率
- 适合医学影像分析与早期皮肤癌筛查研究者参考
若未能及早诊断，皮肤癌可能危及生命，是全球高发且可预防的癌症之一，每年有数百万人被确诊。本文聚焦于良恶性皮肤病灶的判别，评估两种主流深度学习模型——VGG16与DenseNet201在皮肤癌检测中的有效性。基于包含3297张图像的二分类数据集，所有图像经缩放调整为224×224像素，通过深度学习增强技术提升皮肤病变识别能力。实验结果显示，DenseNet201模型在分类任务中取得最高准确率93.79%，显著优于其他方法；尽管如此，仍存在优化空间。未来可通过引入新数据集进一步提升性能，助力早期诊断与临床流程优化。
原文摘要 · Abstract (English)
Skin cancer can be life-threatening if not diagnosed early, a prevalent yet preventable disease. Globally, skin cancer is perceived among the finest prevailing cancers and millions of people are diagnosed each year. For the allotment of benign and malignant skin spots, an area of critical importance in dermatological diagnostics, the application of two prominent deep learning models, VGG16 and DenseNet201 are investigated by this paper. We evaluate these CNN architectures for their efficacy in differentiating benign from malignant skin lesions leveraging enhancements in deep learning enforced to skin cancer spotting. Our objective is to assess model accuracy and computational efficiency, offering insights into how these models could assist in early detection, diagnosis, and streamlined workflows in dermatology. We used two deep learning methods DenseNet201 and VGG16 model on a binary class dataset containing 3297 images. The best result with an accuracy of 93.79% achieved by DenseNet201. All images were resized to 224x224 by rescaling. Although both models provide excellent accuracy, there is still some room for improvement. In future using new datasets, we tend to improve our work by achieving great accuracy.
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