Skin lesion classification enhancement using border-line features – The melanoma vs nevus problem
Pereira, Pedro M. M.
; Paiva, R.
Thomaz, L. A.
Biomedical Signal Processing and Control Vol. 57, Nº 57, pp. 101765 - 101765, March, 2020.
ISSN (print): 1746-8094
Scimago Journal Ranking: 0,77 (in 2020)
Digital Object Identifier: 10.1016/j.bspc.2019.101765
Machine learning algorithms are progressively assuming an important role as a computational tool to support clinical diagnosis, namely in the classification of pigmented skin lesions. The current classification methods commonly rely on features derived from shape, colour, or texture, obtained after image segmentation, but these do not always guarantee the best results. To improve the classification accuracy, this work proposes to further exploit the border-line characteristics of the lesion segmentation mask, by combining gradients with local binary patterns (LBP). In the proposed method, these border-line features are used together with the conventional ones to enhance the performance of skin lesion classification algorithms.
When the new features are combined with the classical ones, the experimental results show higher accuracy, which impacts positively the overall performance of the classification algorithms. As the medical image datasets usually present large class imbalance, which results in low sensitivity for the classifiers, the border-line features have a positive impact on this classification metric, as evidenced by the experimental results. Both the features’ usefulness and their impact are assessed in regard to the classification results, which in turn are statistically tested for completeness, using three different classifiers and two medical image datasets.