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A Study on the Fusion of Pixels and Patient Metadata in CNN-Based Classification of Skin Lesion Images

Fabrizio Nunnari; Chirag Bhuvaneshwara; Abraham Obinwanne Ezema; Daniel Sonntag
In: Andreas Holzinger; Peter Kieseberg; A Min Tjoa; Edgar Weippl (Hrsg.). Machine Learning and Knowledge Extraction. International IFIP Cross Domain (CD) Conference for Machine Learning & Knowledge Extraction (MAKE) (CD-MAKE-2020), August 25-28, Dublin, Ireland, Pages 191-208, ISBN 978-3-030-57321-8, Springer International Publishing, 2020.


We present a study on the fusion of pixel data and patient metadata (age, gender, and body location) for improving the classification of skin lesion images. The experiments have been conducted with the ISIC 2019 skin lesion classification challenge data set. Taking two plain convolutional neural networks (CNNs) as a baseline, metadata are merged using either non-neural machine learning methods (tree-based and support vector machines) or shallow neural networks. Results show that shallow neural networks outperform other approaches in all overall evaluation measures. However, despite the increase in the classification accuracy (up to +19.1%), interestingly, the average per-class sensitivity decreases in three out of four cases for CNNs, thus suggesting that using metadata penalizes the prediction accuracy for lower represented classes. A study on the patient metadata shows that age is the most useful metadatum as a decision criterion, followed by body location and gender.


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