Skin Lesion Classification Using Convolutional Neural Network and ABCD Rule

Abstract

Skin cancer, which can occur in any part of the human skin, is one of the common and serious types of cancer. Accurate diagnosis and segmentation of lesions are crutial to the early diagnosis. Computer-aided diagnosis make important contributions to help doctors in the diagnosis of cancer from skin images. The most important factor for such systems to reveal the accurate results is the correct feature extraction. In this study, a model for the classification of seven types of skin lesions was developed by combining the features of CNN-based feature extraction and the ABCD rule, which is widely used in the clinic. The model was evaluated on HAM10000 well-known dataset. The classification results obtained with different combinations of features and machine learning algorithms were compared. According to the results, the best classification accuracy was obtained with the Cosine Similarity Classifier with 96.4% when the features determined by CNN and the features in the ABCD rule were used together.

Keywords

• Skin cancer
• deep learning
• convolutional neural network
• classification
• ABCD rule

References

1. Slaper, H., Velders, G.J., Daniel, J.S., de Gruijl, F.R., van der Leun, J.C., Estimates of ozone depletion and skin cancer incidence to examine the Vienna Convention achievements, Nature, 21(1996), 256–8.
2. Leiter, U., Eigentler, T., Garbe, C., Epidemiology of skin cancer, Adv Exp Med Biol., 810(2014), 120–140.
3. Didona, D., Paolino, G., Bottoni, U., Cantisani, C., Non-Melanoma Skin Cancer Pathogenesis Overview, Biomedicines, 6(2018), 6.
4. Barton, V., Armeson, K., Hampras, S., Ferris, L.K., Visvanathan, K. et al., Nonmelanoma skin cancer and risk of all-cause and cancer-related mortality: a systematic review, Arch Dermatol Res, 309(2017), 243–251.
5. Chung, S., Basal cell carcinoma, Arch Plast Surg, 39(2012), 166–170.
6. Ray, A., Gupta, A., Al, A., Skin lesion classification with deep convolutional neural network: process development and validation, JMIR Dermatol, 3(2020), 18438.
7. Zambrano-Rom´an, M., Padilla-Guti´errez, J.R., Valle, Y., Mu˜noz-Valle, J.F., Vald´es-Alvarado, E., Non-melanoma skin cancer: A genetic update and future perspectives, Cancers, 14(2022), 2371.
8. Arnold, M., Singh, D., Laversanne, M., Global burden of cutaneous melanoma in 2020 and projections to 2040, JAMA Dermatol, 158(2022), 495-–503.

9. Balch, C.M., Gershenwald, J.E., Soong, S.J., Thompson, J.F., Atkins, M.B. et al., Final version of 2009 AJCC melanoma staging and classification, Journal of clinical oncology, 27(2009), 6199.
10. Lee, C.S., Thomas, C.M., Ng, K.E., An overview of the changing landscape of treatment for advanced melanoma, Pharmacotherapy, 37(2017), 319–333.
11. Bhatia, S., Tykodi, S.S., Thompson, J.A., Treatment of metastatic melanoma: an overview, Oncology, 23(2009), 488–496.
12. Tyrell, R., Antia, C., Stanley, S., Deutsch, G.B., Surgical resection of metastatic melanoma in the era of immunotherapy and targeted therapy, Melanoma Manag, 4(2017), 61–68.
13. Crosby, D., Lyons, N., Greenwood, E., Harrison, S., Hiom, S. et al., A roadmap for the early detection and diagnosis of cancer, The Lancet Oncology, 21(2020), 1397–1399.
14. Alendar, F., Drljevi´c, I., Drljevi´c, K., Alendar, T., Early detection of melanoma skin cancer, Bosn J Basic Med Sci, 9(2009), 77–80.
15. Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M. et al. Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542(2017), 115–118.
16. Haenssle, H.A., Fink, C., Schneiderbauer, R., Toberer, F., Buhl, T. et al., Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists, Ann Oncol, 29(2018), 1836–1842.
17. Tschandl, P., Rinner, C., Apalla, Z., Argenziano, G., Codella, N. et al., Human-computer collaboration for skin cancer recognition, Nature Medicine, 26(2020), 1229–1234.
18. Stanley, R.J., Stoecker, W.V., Moss, R.H., A relative color approach to color discrimination for malignant melanoma detection in dermoscopy images, Skin Res. Technol, 13(2007), 62–72.
19. Ballerini, L., Fisher, R.B., Aldridge, B., Rees, J., A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non- Melanoma Skin Lesions, Color Medical Image Analysis, New York, 2013.
20. Stoecker, W.V., Wronkiewiecz, M., Chowdhury, R., Stanley, R.J., Xu, J. et al., Detection of granularity in dermoscopy images of malignant melanoma using color and texture features, Comput Med Imaging Graph, 35(2011), 144–7.
21. Celebi, M.E., Kingravi, H.A., Uddin, B., Iyatomi H, Aslandogan Y.A. et al. A methodological approach to the classification of dermoscopy images, Comput Med Imaging Graph, 31(2007), 362–373.
22. Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks, Communications of the ACM, 60(2017), 84–90.
23. Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z. et al., Deep convolutional neural networks for computer-aided detection: CNN architectures dataset characteristics and transfer learning, IEEE Trans. Med. Imag, 35(2016), 1285–1298.
24. Dandan, Z., Yang, L., Hongpeng, Y., Zhiqiang, W., A novel multi-scale CNNs for false positive reduction in pulmonary nodule detection, Expert Systems with Applications, 207(2022), 117652.
25. Mutasa, S., Sun, S., Ha, R., Understanding artificial intelligence based radiology studies: CNN architecture, Clinical Imaging, 80(2021), 72–76.
26. Schwendicke, F., Golla, T., Dreher, M., Krois, J., Convolutional neural networks for dental image diagnostics: A scoping review, Journal of Dentistry, 91(2019), 103226.
27. Codella, N., Cai, J., Abedini, M., Garnavi, R., Halpern, A. et al., Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images, Machine Learning in Medical Imaging, Springer, 2015.
28. Yu, L., Chen, H., Dou, Q., Qin, J., Heng, P.A., Automated melanoma recognition in dermoscopy images via very deep residual networks, IEEE Transactions on Medical Imaging, 36(2017), 994–1004.
29. Majtner, T., Yildirim-Yayilgan, S., Hardeberg, J.Y., Optimised deep learning features for improved melanoma detection, Multimed Tools Appl, 78(2019), 11883-–11903.
30. Acosta, M.F.J., Tovar, L.Y.C., Garcia-Zapirain, M.B., Percybrooks, W.S., Melanoma diagnosis using deep learning techniques on dermatoscopic images, BMC Med Imaging, 21(2021), 6.
31. Afza, F., Sharif, M., Mittal, M., Khan, M.A., Hemanth, D.J., A hierarchical three-step superpixels and deep learning framework for skin lesion classification, Methods, 202(2022), 88–102.
32. Jin, Q., Cui, H., Sun, C., Meng, Z., Su, R., Cascade knowledge diffusion network for skin lesion diagnosis and segmentation, Applied Soft Computing, 99(2021), 106881.
33. Tschandl, P., Rosendahl, C., Kittler, H., The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions, Sci. Data, 5(2018), 180161.
34. Senan, E., Jadhav, M., Analysis of dermoscopy images by using ABCD rule for early detection of skin cancer, Global Transitions Proceedings, 2(2021), 1–7.
35. Cheng, H.D., Shan, J., Ju, W., Guo, Y., Zhang, L., Automated breast cancer detection and classification using ultrasound images: A survey, Pattern Recognition, 43(2010), 299—317.
36. Chollet, F., Xception: Deep learning with depthwise separable convolutions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2017), 1251–1258.
37. Liu, Y., Zhang, L., Hao, Z., Yang, Z.,Wang, S. et al., An Xception model based on residual attention mechanism for the classification of benign and malignant gastric ulcers, Sci Rep, 12(2022), 15365.
38. Moataz, L., Salama, G., Elazeem, M., Skin cancer diseases classification using deep convolutional neural network with transfer learning model, Journal of Physics: Conference Series, (2021), 2128.
39. Lu, X., Zadeh, Y.A., Deep Learning-Based Classification for Melanoma Detection Using XceptionNet, Journal of Healthcare Engineering, (2022), 1–10.
40. Coye, T., Novel Method for Determining Symmetry of Skin Lesions using the Jaccard Index, MATLAB Central File Exchange, 2015, (https://www.mathworks.com/matlabcentral/fileexchange/50903-novel-method-for determining-symmetry-of-skin-lesionsusing- the-jaccard-index), Retrieved January 3, 2023.
41. Katz, M.J., Fractals and the analysis of waveforms, Computers in Biology and Medicine, 18(1988), 145-–156.
42. Chatterjee, S., Dey, D., Munshi, S., Optimal selection of features using wavelet fractal descriptors and automatic correlation bias reduction for classifying skin lesions, Biomedical Signal Processing and Control, 40(2018), 252–262.
43. Chatterjee, S., Dey, D., Munshi, S., Gorai, S., Dermatological expert system implementing the ABCD rule of dermoscopy for skin disease identification, Expert Systems with Applications, 167(2021), 114204.
44. Coye, T., Tyler Coye (2023). Function for Counting Colors in a Skin Lesion, MATLAB Central File Exchange, 2015, (https://www.mathworks.com/matlabcentral/fileexchange/50872-function-for-counting-colors-in-a-skin lesion), Retrieved January 4, 2023.