  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.
