Automated Segmentation of Psoriasis in Uncontrolled Environments Using a Three-Class Ensemble Architecture

Psoriasis is a heterogeneous inflammatory skin disease requiring continuous monitoring to assess treatment efficacy. Automated lesion segmentation remains a significant computer vision challenge due to irregular plaque boundaries, variable skin tones, and uncontrolled lighting conditions in clinical photography. This study proposes a robust hybrid deep learning framework for the automated segmentation of psoriatic lesions in unconstrained environments. We constructed a unique dataset utilizing a hierarchical three-class labeling scheme (psoriatic plaque, healthy skin, and background) to mitigate the class imbalance and background noise often found in binary segmentation tasks. Following a systematic hyperparameter optimization using the Optuna framework, three distinct architectures—DeepLabV3+, UperNet, and SegFormer—were identified as optimal. A novel ensemble architecture was then developed to integrate the high sensitivity of DeepLabV3+, the precision of UperNet, and the contextual balance of SegFormer via a conflict-resolution voting algorithm. Experimental results demonstrate that the proposed hybrid model outperforms individual state-of-the-art architectures, achieving a Dice coefficient of 89.3% for lesion segmentation and an F1 score of 90.7% across skin classes. These findings confirm the system’s adaptability to real-world imaging conditions, validating its potential as an objective decision-support tool for dermatological practice.