Home > Articles > All Issues > 2023 > Volume 11, No. 2, June 2023 >
JOIG 2023 Vol.11(2): 161-169
doi: 10.18178/joig.11.2.161-169

Mobile Dermatoscopy: Class Imbalance Management Based on Blurring Augmentation, Iterative Refining and Cost-Weighted Recall Loss

Nauman Ullah Gilal*, Samah Ahmed Mustapha Ahmed, Jens Schneider, Mowafa Househ, and Marco Agus
College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar;
Email: saah33911@hbku.edu.qa (S.A.M.A.), jeschneider@hbku.edu.qa (J.S.), mhouseh@hbku.edu.qa (M.H.),magus@hbku.edu.qa (M.A.)
*Correspondence: giul30541@hbku.edu.qa (N.U.G.)

Manuscript received December 7, 2022; revised March 21 2023; accepted May 3, 2023.

Abstract—We present an end-to-end framework for real-time melanoma detection on mole images acquired with mobile devices equipped with off-the-shelf magnifying lens. We trained our models by using transfer learning through EfficientNet convolutional neural networks by using public domain The International Skin Imaging Collaboration (ISIC)-2019 and ISIC-2020 datasets. To reduce the class imbalance issue, we integrated the standard training pipeline with schemes for effective data balance using oversampling and iterative cleaning through loss ranking. We also introduce a blurring scheme able to emulate the aberrations produced by commonly available magnifying lenses, and a novel loss function incorporating the difference in cost between false positive (melanoma misses) and false negative (benignant misses) predictions. Through preliminary experiments, we show that our framework is able to create models for real-time mobile inference with controlled tradeoff between false positive rate and false negative rate. The obtained performances on ISIC-2020 dataset are the following: accuracy 96.9%, balanced accuracy 98%, ROCAUC=0.98, benign recall 97.7%, malignant recall 97.2%.

Keywords—melanoma detection, The International Skin Imaging Collaboration (ISIC) dataset, mobile dermatoscopy, class imbalance, refining, recall loss

Cite: Nauman Ullah Gilal, Samah Ahmed Mustapha Ahmed, Jens Schneider, Mowafa Househ, and Marco Agus, "Mobile Dermatoscopy: Class Imbalance Management Based on Blurring Augmentation, Iterative Refining and Cost-Weighted Recall Loss," Journal of Image and Graphics, Vol. 11, No. 2, pp. 161-169, June 2023.



Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.