Towards Improved Skin Lesion Classification using Metadata Supervision

Skin cancer is one of the most common types of cancer worldwide. Nowadays, Computer-Aided Diagnosis (CAD) systems are being adopted to diagnose skin cancer as they help reduce the manual burden on doctors and provide high reliability. Most of the contributions made towards developing algorithms for skin lesion classification have mainly considered the imaging modality only. However, in practical scenarios, it has been found that dermatologists also consider the patient’s demographics to refine their outcome. Based on this fact, this paper proposes a multimodal fusion-based approach toward improved skin lesion classification using patient metadata. We have presented a novel algorithm that combines the patient’s clinical information to guide the image features to improve the skin lesion classification. Also, the proposed work solved the issue of missing and unknown values in the patients’ metadata to assist in further improvements in skin cancer classification. The proposed approach is validated through extensive experimentation and surpasses available state-of-the-art methods on the benchmark dataset (PAD-UFES-20). We have evaluated the proposed method quantitatively and qualitatively and found it robust enough to classify skin lesion categories effectively. The proposed approach outperformed the benchmark results significantly over five Convolutional Neural Network (CNN) architectures during the evaluation. Our results can be reproduced
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