Melanoma is the most aggressive form of skin cancer, and early detection can
significantly increase survival rates and prevent cancer spread. However,
developing reliable automated detection techniques is difficult due to the lack
of standardized datasets and evaluation methods. This study introduces a
unified melanoma classification approach that supports 54 combinations of 11
datasets and 24 state-of-the-art deep learning architectures. It enables a fair
comparison of 1,296 experiments and results in a lightweight model deployable
to the web-based MeshNet architecture named Mela-D. This approach can run up to
33x faster by reducing parameters 24x to yield an analogous 88.8\% accuracy
comparable with ResNet50 on previously unseen images. This allows efficient and
accurate melanoma detection in real-world settings that can run on
consumer-level hardware.

通过引入支持 11 个数据集和 24 种先进深度学习架构的统一黑素瘤分类方法，该研究建立了一个可比较 1,296 个实验的公平对比，并生成一个可在 Web 上部署的轻量级模型（称为 Mela-D）以及减少参数 24 倍以提高 33 倍运行速度，并在之前未见过的图像上获得与 ResNet50 相似的 88.8% 的准确率，从而实现在消费级硬件上高效准确地在实际环境中检测黑素瘤。