CS-AF: A cost-sensitive multi-classifier active fusion framework for skin lesion classification

Abstract

Convolutional neural networks (CNNs) have achieved the state-of-the-art performance in skin lesion analysis. Compared with single CNN classifier, combining the results of multiple classifiers via fusion approaches shows to be more effective and robust. Since the skin lesion datasets are usually limited and statistically biased, while designing an effective fusion approach, it is important to consider not only the performance of each classifier on the training/validation dataset, but also the relative discriminative power (e.g., confidence) of each classifier regarding an individual sample in the testing phase, which calls for an active fusion approach. Furthermore, in skin lesion analysis, the data of certain classes (e.g., the benign lesions) is usually abundant which makes them an over-represented majority, while the data of some other classes (e.g., the cancerous lesions) is deficient which makes them an underrepresented minority. It is more crucial to precisely identify the samples from an underrepresented (i.e., in terms of the amount of data) but more important minority class (e.g., cancerous skin lesions). In other words, misclassifying a more severe skin lesion to a benign or less severe skin lesion should have relative more cost (e.g., money, time and even lives). To address such challenges, we present CS-AF, a cost-sensitive multi-classifier active fusion framework for skin lesion classification. In the experimental evaluation, we prepared 96 base classifiers (of 12 CNN architectures) on the ISIC Challenge 2019 research dataset. Our experimental results show that our framework consistently outperforms both the static and the active fusion competitors in terms of the accuracy and total costs.