Web-based Melanoma Detection
This enables efficient and accurate melanoma detection for medical applications, but it is incremental as it builds on existing deep learning methods.
The study tackled the problem of automated melanoma detection by introducing a unified classification approach that supports multiple datasets and architectures, resulting in a lightweight model (Mela-D) that runs up to 33x faster with 88.8% accuracy comparable to ResNet50.
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.