Web-based Melanoma Detection

TL;DR

This paper presents a unified, web-deployable melanoma detection model that supports multiple datasets and architectures, achieving high accuracy with significantly reduced computational requirements for real-world use.

Contribution

Introduces a comprehensive melanoma classification framework supporting numerous datasets and architectures, enabling fair comparison and resulting in a lightweight, fast, and accurate web-based detection model.

Findings

Supports 54 dataset combinations and 24 architectures.

Achieves 88.8% accuracy comparable to ResNet50.

Runs up to 33x faster with 24x fewer parameters.

Abstract

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.