UlcerMTL: Multi-Task Learning for Classification and Segmentation of Diabetic Foot Ulcers

Early identification and management of Diabetic Foot Ulcers (DFU) are critical for preventing severe complications, particularly in the Indian subcontinent where DFU prevalence is high. This paper introduces UlcerMTL, a multi-task learning (MTL) model designed to simultaneously perform binary classification (ulcer vs. healthy skin) and semantic segmentation of the ulcer boundaries. Our approach leverages a shared SegFormer-B2 encoder to extract common features, which are then passed to task-specific classification and segmentation heads. This enables effective specialization for each task while benefiting from shared early representations. We evaluated our model on a newly collected dataset comprising 1,534 diabetic foot ulcer patches and 1,533 healthy skin patches from patients in the Indian subcontinent. Our model achieves a classification accuracy of 99.7%, with a segmentation performance yielding an Intersection over Union (IoU) of 0.709 and a Dice score of 0.829. Compared to single-task baselines, our approach offers significant improvements, demonstrating strong potential for clinical deployment in DFU screening and management.