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This study presents a new hair removal algorithm for dermatoscopic images of skin lesions that improves hair detection accuracy by 2–7% and hair repair accuracy by 2–5% on average, using advanced techniques like maximum variance fuzzy clustering, Criminisi priorities, and the ant colony algorithm.

This study reviews methods to ensure the reliability of machine learning models in medical imaging, focusing on bias detection, data drift assessment, and accuracy estimation without ground truth labels to enhance integration into clinical settings.

This study proposes a method for automatically removing hairs from skin lesion images by using edge-tangent flow for hair detection and texture synthesis for restoring occluded regions with minimal artifacts.

This study introduced a single-stage network using large kernel attention that effectively restores high-resolution images by capturing both global and local details, reducing parameters and improving processing speed.

This case report from the study describes a dog suffering from dermatophytosis caused by Microsporum canis, presenting with alopecia on the neck and ear, illustrating the zoonotic risk of these fungal infections in both animals and humans, particularly in hot and humid climates.