Recent work on the classification of microscopic skin lesions does not consider how the presence of skin hair may affect diagnosis. In this work, we investigate how deep-learning models can handle a varying amount of skin hair during their predictions. We present an automated processing pipeline that tests the performance of the classification model. We conclude that, under realistic conditions, modern day classification models are robust to the presence of skin hair and we investigate three architectural choices (Resnet50, InceptionV3, Densenet121) that make them so.
|Title of host publication||Pattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings|
|Editors||Alberto Del Bimbo, Rita Cucchiara, Stan Sclaroff, Giovanni Maria Farinella, Tao Mei, Marco Bertini, Hugo Jair Escalante, Roberto Vezzani|
|Publisher||Springer Science and Business Media B.V.|
|Number of pages||11|
|Publication status||Published - 2021|
|Event||25th International Conference on Pattern Recognition Workshops, ICPR 2020 - Milan, Italy|
Duration: 10 Jan 2021 → 11 Jan 2021
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||25th International Conference on Pattern Recognition Workshops, ICPR 2020|
|Period||10/01/21 → 11/01/21|
Bibliographical notePublisher Copyright:
© 2021, Springer Nature Switzerland AG.
- Deep learning
- Hair detection
- Skin lesion