Abstract
This paper investigates the visual classification of the 10 skin lesions most commonly encountered in a clinical setting (including melanoma (MEL) and melanocytic nevi (ML)), unlike the majority of previous research that focuses solely on melanoma versus melanocytic nevi classification. Two families of architectures are explored: (1) semi-learned hierarchical classifiers and (2) deep net classifiers. Although many applications have benefited by switching to a deep net architecture, here there is little accuracy benefit: hierarchical KNN classifier 78.1%, flat deep net 78.7% and refined hierarchical deep net 80.1% (all 5 fold cross-validated). The classifiers have comparable or higher accuracy than the five previous research results that have used the Edinburgh DERMOFIT 10 lesion class dataset. More importantly, from a clinical perspective, the proposed hierarchical KNN approach produces: (1) 99.5% separation of melanoma from melanocytic nevi (76 MEL & 331 ML samples), (2) 100% separation of melanoma from seborrheic keratosis (SK) (76 MEL & 256 SK samples), and (3) 90.6% separation of basal cell carcinoma (BCC) plus squamous cell carcinoma (SCC) from seborrheic keratosis (SK) (327 BCC/SCC & 256 SK samples). Moreover, combining classes BCC/SCC & ML/SK to give a modified 8 class hierarchical KNN classifier gives a considerably improved 87.1% accuracy. On the other hand, the deepnet binary cancer/non-cancer classifier had better performance (0.913) than the KNN classifier (0.874). In conclusion, there is not much difference between the two families of approaches, and that performance is approaching clinically useful rates.
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References
American Cancer Society. Cancer Facts & Figures (2016)
Ballerini, L., Fisher, R.B., Aldridge, R.B., Rees, J.: A color and texture based hierarchical K-NN approach to the classification of non-melanoma skin lesions. In: Celebi, M.E., Schaefer, G. (eds.) Color Medical Image Analysis. Lecture Notes in Computer Vision and Biomechanics, vol. 6. Springer, Dordrecht (2013). https://doi.org/10.1007/978-94-007-5389-1_4
Bertrand, A.: Classification of skin lesions images using deep nets. Intern report, INP Grenoble (2018)
Di Leo, C., Bevilacqua, V., Ballerini, L., Fisher, R., Aldridge, B., Rees, J.: Hierarchical classification of ten skin lesion classes. In: Proceedings of Medical Image Analysis Workshop, Dundee (2015)
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017)
Ferris, L.K., et al.: Computer-aided classification of melanocytic lesions using dermoscopic images. J. Am. Acad. Dermatol. 73(5), 769–776 (2015)
Haralick, R.M., Shanmungam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. B Cybern. 3(6), 610–621 (1973)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of CVPR (2016)
Kawahara, J., BenTaieb, A., Hamarneh, G.: Deep features to classify skin lesions. In: Proceedings of IEEE 13th International Symposium on Biomedical Imaging (ISBI) (2016)
Kawahara, J., Hamarneh, G.: Multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained layers. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, H.-I. (eds.) MLMI 2016. LNCS, vol. 10019, pp. 164–171. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47157-0_20
Korotkov, K., Garcia, R.: Computerized analysis of pigmented skin lesions: a review. Artif. Intell. Med. 56(2), 69–90 (2012)
Li, X., Aldridge, B., Ballerini, L., Fisher, R., Rees, J.: Depth data improves skin lesion segmentation. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 1100–1107. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04271-3_133
Maglogiannis, I., Doukas, C.N.: Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans. Inf. Technol. Biomed. 13(5), 721–733 (2009)
Masood, A., Al-Jumaily, A.A.: Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms. Int. J. Biomed. Imaging 2013, 22 (2013)
Perez, F., Vasconcelos, C., Avila, S., Valle, E.: Data augmentation for skin lesion analysis. In: Stoyanov, D., et al. (eds.) CARE/CLIP/OR 2.0/ISIC -2018. LNCS, vol. 11041, pp. 303–311. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01201-4_33
Acknowledgments
The research was part of the DERMOFIT project (homepages.inf.ed.ac.uk/rbf/DERMOFIT) funded originally by the Wellcome Trust (Grant No: 083928/Z/07/Z). Much of the ground-truthing, feature extraction and image analysis was done by colleagues B. Aldridge, L. Ballerini, C. Di Leo, and X. Li as reported previously.
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Fisher, R.B., Rees, J., Bertrand, A. (2020). Classification of Ten Skin Lesion Classes: Hierarchical KNN versus Deep Net. In: Zheng, Y., Williams, B., Chen, K. (eds) Medical Image Understanding and Analysis. MIUA 2019. Communications in Computer and Information Science, vol 1065. Springer, Cham. https://doi.org/10.1007/978-3-030-39343-4_8
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