Skin lesion classification enhancement using border-line features – The melanoma vs nevus problem. (March 2020)

• Record Type: Journal Article
• Authors: Pereira, Pedro M.M.; Fonseca-Pinto, Rui; Paiva, Rui Pedro; Assuncao, Pedro A.A.; Tavora, Luis M.N.; Thomaz, Lucas A.; Faria, Sergio M.M.
• Is Part Of: Biomedical signal processing and control. Volume 57(2020)
• Publication Date: 2020-03
• Subjects: Medical imaging -- Skin lesion -- Image segmentation -- Feature extraction -- Classification; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.101765
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Twenty-two border-line features are introduced to improve classification results. Statistical relevance of the features is verified through a series of experiments. Experiments cover two datasets, three classifiers, and two segmentation methods. Detailed lesion contours provide better border-line features than coarser ones. Border-line features increase classification sensibility on unbalanced dataset. Abstract: 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.