Lightweight Attention-Based Architecture for Accurate Melanoma Recognition

Dermoscopy, a non-invasive imaging technique, has transformed dermatology by enabling early detection and differentiation of skin conditions. Integrating deep learning with dermoscopic images enhances diagnostic potential but raises computational challenges. This study introduces APNet, an attention-based architecture designed for melanoma detection, offering fewer parameters than conventional convolutional neural networks. Two baseline models are considered: HU-Net, a trimmed U-Net that uses only the encoding path for classification, and Pocket-Net, a lightweight U-Net variant that reduces parameters through fewer feature maps and efficient convolutions. While Pocket-Net is highly resource-efficient, its simplification can reduce performance. APNet extends Pocket-Net by incorporating squeeze-and-excitation (SE) attention blocks into the encoding path. These blocks adaptively highlight the most relevant dermoscopic features, such as subtle melanoma patterns, improving classification accuracy. The study evaluates APNet against Pocket-Net and HU-Net using four large, annotated dermoscopy datasets (ISIC 2017–2020), covering melanoma, benign nevi, and other lesions. Results show that APNet achieves faster processing than HU-Net while overcoming the performance loss observed in Pocket-Net. By reducing parameters without sacrificing accuracy, APNet provides a practical solution for computationally demanding dermoscopy, offering efficient and accurate melanoma detection where medical imaging resources are limited.