Skin lesion segmentation and recognition using multichannel saliency estimation and M-SVM on selected serially fused features

被引：50
作者：
Akram T. [1 ]
Khan M.A. [2 ]
Sharif M. [3 ]
Yasmin M. [3 ]
机构：
[1] Department of Electrical Engineering, COMSATS University Islamabad, Wah Campus, Wah Cantt
[2] Department of Compute Science and Engineering, HITEC Univesity, Museum Road, Taxila
[3] Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt
来源：
Journal of Ambient Intelligence and Humanized Computing | 2024年 / 15卷 / 01期
关键词：
Feature extraction; Feature selection; Image enhancement; Image fusion; Skin cancer;
D O I：
10.1007/s12652-018-1051-5
中图分类号：
学科分类号：
摘要：
The number of deaths caused by melanoma has increased remarkably in the last few years which are the carcinogenic type of skin cancer. Lately, computer based methods are introduced which are intelligent enough to support dermatologist in initial judgment of skin lesion. However, there still exists a gap for an optimal solution; therefore, machine learning community is still considering it a great challenge. The primary objective of this article is to efficiently detect and classify skin lesion with the utilization of an improved segmentation and feature selection criteria. Presented contribution is threefold; First, ternary color spaces are exploited to separate foreground from the background—utilizing multilevel approach of contrast stretching. Second, a weighting criterion is designed which is able to select the best solution based on extended texture feature analysis, related labels, boundary connections and central distance. Third, an improved feature extraction and dimensionality reduction criteria is proposed which combines conventional as well as recent feature extraction techniques. The proposed method is tested on PH2, ISBI 2016 and ISIC benchmark data sets and evaluated on the basis of multiple parameters including FPR, sensitivity, specificity, FNR and accuracy. From the statistics, it is quite clear that the proposed method outperforms numerous existing techniques with considerable margin. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.
引用
收藏
页码：1083 / 1102
页数：19
相关论文
共 75 条
  • [41] Li J., Liu L., Zhou M., Yang J.-J., Chen S., Liu H., Tan F., Feature selection and prediction of small-for-gestational-age infants, J Ambient Intell Humaniz Comput, (2018)
  • [42] Liaqat A., Khan M.A., Shah J.H., Sharif M., Yasmin M., Fernandes S.L., Automated ulcer and bleeding classification from wce images using multiple features fusion and selection, J Mech Med Biol, 18, 4, (2018)
  • [43] Liu Y., Zheng Y.F., One-against-all multi-class SVM classification using reliability measures, In: Paper Presented at the Neural Networks, 2005, (2005)
  • [44] Lopez A.R., Giro-I-nieto X., Burdick J., Marques O., Skin lesion classification from dermoscopic images using deep learning techniques, In: Paper Presented at the Biomedical Engineering (Biomed), 2017 13Th IASTED International Conference On, (2017)
  • [45] Machado M., Pereira J., Fonseca-Pinto R., Classification of reticular pattern and streaks in dermoscopic images based on texture analysis, J Med Imaging, 2, 4, (2015)
  • [46] Majtner T., Yildirim-Yayilgan S., Hardeberg J.Y., Combining deep learning and hand-crafted features for skin lesion classification, In: Paper Presented at the Image Processing Theory Tools and Applications (IPTA), 2016 6Th International Conference On, (2016)
  • [47] Monisha M., Suresh A., Bapu B.T., Rashmi M., Classification of malignant melanoma and benign skin lesion by using back propagation neural network and ABCD rule, Cluster Comput, pp. 1-11, (2018)
  • [48] Nasir M., Attique Khan M., Sharif M., Lali I.U., Saba T., Iqbal T., An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach, Microsc Res Tech, 81, 6, pp. 528-543, (2018)
  • [49] Okuboyejo D.A., Olugbara O.O., Odunaike S.A., Automating skin disease diagnosis using image classification, In: Paper Presented at the Proceedings of the World Congress on Engineering and Computer Science, (2013)
  • [50] Oliveira R.B., Papa J.P., Pereira A.S., Tavares J.M.R., Computational methods for pigmented skin lesion classification in images: review and future trends, Neural Comput Appl, 29, 3, pp. 613-636, (2018)
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