Tackling the ordinal and imbalance nature of a melanoma image classification problem

Melanoma is a type of cancer that usually occurs on the skin. Early detection is crucial for ensuring five-year survival (which varies between 15% and 99% depending on the melanoma stage). Melanoma severity is typically diagnosed by invasive methods (e.g. a biopsy). In this paper, we propose an alternative system combining image analysis and machine learning for detecting melanoma presence and severity. The 86 features selected consider the shape, colour, pigment network and texture of the melanoma. As opposed to previous studies that have focused on distinguishing melanoma and non-melanoma images, our work considers a finer-grain classification problem using five categories: benign lesions and 4 different stages of melanoma. The dataset presents two main characteristics that are approached by specific machine learning methods: 1) the classes representing melanoma severity follow a natural order, and 2) the dataset is imbalanced, where benign lesions clearly outnumber melanoma ones. Different nominal and ordinal classifiers are considered, one of them being based on an ordinal cascade decomposition method. The cascade method is shown to obtain good performance for all classes, while respecting and exploiting the order information. Moreover, we explore the alternative of applying a class balancing technique, presenting good synergy with the ordinal and nominal methods.