Improving Skin Lesion Prediction Performance by Integrating Clinical Metadata

Skin cancer is one of the most common cancers, requiring early anticipation to improve treatment efficiency. Expected-aided computer systems, especially deep learning models, often only use images while ignoring available clinical information such as age, gender, and the anatomical location of the lesion. This research introduces the new method based on MaxViT architecture, which simultaneously combines specific images and available data to classify 7 types of lesions in the HAM10000 dataset. The proposed model uses MaxViT to extract specific images, combined with the technique of handling missing data for age field. The cross-attention fusion mechanism helps to refine image features based on clinical data embedding. With the training strategy of two stages and Layer-wise Learning Rate Decay, the proposed model achieves 92.16% accuracy, 87.18% macro Precision, 87.77% macro Recall, and 87.32% macro F1-score, which are superior to the image only model. The results confirm that integrating clinical data through the fusion mechanism significantly improves the accuracy and reliability of the automatic skin cancer diagnosis system.