A deep learning-based dual-branch framework for automated skin lesion segmentation and classification via dermoscopic Images.

Early skin disease detection significantly improves patient survival rates, yet limited access to dermatological expertise creates an urgent need for automated diagnostic systems. In this paper, we develop a dual-branch deep learning framework that simultaneously performs skin lesion segmentation and classification from dermoscopic images. The proposed segmentation branch uses a modified EfficientNet-B7 encoder with Atrous Spatial Pyramid Pooling (ASPP) for multi-scale feature extraction and transformer blocks for global context modeling. Attention gates and Squeeze-and-Excitation blocks enhance feature selection and boundary precision. The classification branch fuses DenseNet-121 visual features with morphological characteristics extracted from predicted segmentation masks, creating a hybrid appearance-morphology analysis approach. The proposed framework achieved strong and consistent segmentation performance across five benchmark datasets. On HAM10000, the highest Dice score (0.9568) and IoU (0.9242) were recorded, with an accuracy of 0.9708. PH2 achieved a Dice of 0.9250 and a sensitivity of 0.9734, while ISIC 2016 reached a Dice of 0.9298 and an IoU of 0.8811. For ISIC 2017 and ISIC 2018, Dice scores were 0.8972 and 0.9020, respectively. All datasets reported high specificity (> 0.93) and accuracy (> 0.95), confirming the model's robustness and generalization capability. Our dual-branch framework achieves state-of-the-art accuracy by effectively integrating visual appearance and structural morphological features for comprehensive skin lesion analysis. The consistent high performance across diverse datasets indicates strong potential for clinical deployment as a diagnostic support tool.