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.

翻译：皮肤癌若未能及早诊断可能危及生命，这是一种普遍但可预防的疾病。在全球范围内，皮肤癌被视为最常见的癌症之一，每年有数百万人被确诊。针对皮肤良性与恶性斑点的区分——这是皮肤病学诊断中至关重要的领域，本文研究了两种主流的深度学习模型VGG16和DenseNet201的应用。我们评估了这些CNN架构在区分良性与恶性皮肤病变方面的效能，利用了深度学习在皮肤癌检测领域实施的增强技术。我们的目标是评估模型的准确性与计算效率，从而揭示这些模型如何协助皮肤病学中的早期检测、诊断及优化工作流程。我们在一个包含3297张图像的二分类数据集上使用了DenseNet201和VGG16两种深度学习方法。DenseNet201取得了最佳结果，准确率达到93.79%。所有图像通过重新缩放调整为224x224尺寸。尽管两种模型均提供了优异的准确率，但仍存在一定的改进空间。未来通过使用新的数据集，我们致力于提升工作成果以实现更高的准确率。