-
1
Skin lesions vary in type and severity, ranging from benign conditions like moles to malignant ones such as melanoma, necessitating accurate classification.
-
2
Timely identification of skin lesions is crucial to prevent progression to serious conditions, with melanoma being a leading cause of skin cancer mortality.
-
3
Traditional methods for skin lesion detection face challenges, including human error and reliance on examiner skill, highlighting the need for improved techniques.
-
4
Artificial intelligence, particularly machine learning and deep learning, shows promise in automating skin lesion detection but faces issues like class imbalance and model integration.
-
5
The proposed framework utilizes hierarchical attention and ensemble learning to enhance skin lesion classification accuracy by addressing key challenges in existing methods.
