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Skin Lesion Analyser: An Efficient Seven-Way Multi-class Skin Cancer Classification Using MobileNet

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Advanced Machine Learning Technologies and Applications (AMLTA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1141))

Abstract

Skin cancer is an emerging global health problem with 123,000 melanoma and 3,000,000 non-melanoma cases worldwide each year. The recent studies have reported excessive exposure to ultraviolet rays as a major factor in developing skin cancer. The most effective solution to control the death rate for skin cancer is a timely diagnosis of skin lesions as the five-year survival rate for melanoma patients is 99% when diagnosed and screened at the early stage. Considering an inability of dermatologists for accurate diagnosis of skin cancer, there is a need to develop an automated efficient system for the diagnosis of skin cancer. This study explores an efficient automated method for skin cancer classification with better evaluation metrics as compared to previous studies or expert dermatologists. We utilized a MobileNet model pretrained on approximately 1,280,000 images from 2014 ImageNet Challenge and finetuned on 10,015 dermoscopy images of HAM10000 dataset employing transfer learning. The model used in this study achieved an overall accuracy of 83.1% for seven classes in the dataset, whereas top2 and top3 accuracies of 91.36% and 95.34%, respectively. Also, the weighted average of precision, weighted average of recall, and weighted average of f1-score were found to be 89%, 83%, and 83%, respectively. This method has the potential to assist dermatology specialists in decision making at critical stages. We have deployed our deep learning model at https://saketchaturvedi.github.io as Web application.

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References

  1. Stewart, B.W., Wild, C.: International Agency for Research on Cancer, and World Health Organization. World cancer report (2014)

    Google Scholar 

  2. Cakir, B.O., Adamson, P., Cingi, C.: Epidemiology and economic burden of nonmelanoma skin cancer. Facial Plast. Surg. Clin. North Am. 20(4), 419–422 (2012). https://doi.org/10.1016/j.fsc.2012.07.004

    Article  Google Scholar 

  3. Rogers, H.W., Weinstock, M.A., Feldman, S.R., Coldiron, B.M.: Incidence estimate of nonmelanoma skin cancer (Keratinocyte carcinomas) in the U.S. population, 2012. JAMA Dermatol. 151(10), 1081–1086 (2015). https://doi.org/10.1001/jamadermatol.2015.1187

    Article  Google Scholar 

  4. Stern, R.S.: Prevalence of a history of skin cancer in 2007: results of an incidence-based model. Arch. Dermatol. 146(3), 279–282 (2010). https://doi.org/10.1001/archdermatol.2010.4

    Article  MathSciNet  Google Scholar 

  5. WHO: Skin cancers WHO (2017)

    Google Scholar 

  6. Koh, H.K., Geller, A.C., Miller, D.R., Grossbart, T.A., Lew, R.A.: Prevention and early detection strategies for melanoma and skin cancer current status. Arch. Dermatol. 132(4), 436–443 (1996). https://doi.org/10.1001/archderm.1996.03890280098014

    Article  Google Scholar 

  7. Parkin, D.M., Mesher, D., Sasieni, P.: Cancers attributable to solar (ultraviolet) radiation exposure in the UK in 2010. Br. J. Cancer 105(2), S66–S69 (2011). https://doi.org/10.1038/bjc.2011.486

    Article  Google Scholar 

  8. Canadian Cancer Society. Risk factors for melanoma skin cancer (2018). https://www.cancer.org/cancer/melanoma-skin-cancer/causes-risks-prevention/risk-factors.html. Accessed 31 Mar 2019

  9. Cancer facts & figures 2016. Atlanta, American Cancer Society (2016). https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2016.html. Accessed 31 Mar 2019

  10. Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2017. CA Cancer J. Clin. 67(1), 7–30 (2017). https://doi.org/10.3322/caac.21387

    Article  Google Scholar 

  11. Neville, J.A., Welch, E., Leffell, D.J.: Management of nonmelanoma skin cancer in 2007. Nat. Clin. Pract. Oncol. 4(8), 462–469 (2007). https://doi.org/10.1038/ncponc0883

    Article  Google Scholar 

  12. Morton, C.A., Mackie, R.M.: Clinical accuracy of the diagnosis of cutaneous malignant melanoma. Br. J. Dermatol. 138(2), 283–287 (1998). https://doi.org/10.1046/j.1365-2133.1998.02075.x

    Article  Google Scholar 

  13. Binder, M., Schwarz, M., Winkler, A., et al.: Epiluminescence microscopy. A useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists. Arch. Dermatol. 131(3), 286–291 (1995). https://doi.org/10.1001/archderm.1995.01690150050011

    Article  MathSciNet  Google Scholar 

  14. Piccolo, D., Ferrari, A., Peris, K., Daidone, R., Ruggeri, B., Chimenti, S.: Dermoscopic diagnosis by a trained clinician vs. a clinician with minimal dermoscopy training vs. computer-aided diagnosis of 341 pigmented skin lesions: a comparative study. Br. J. Dermatol. 147(3), 481–486 (2002). https://doi.org/10.1046/j.1365-2133.2002.04978.x

    Article  Google Scholar 

  15. Argenziano, G., et al.: Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. J. Am. Acad. Dermatol. 48(5), 679–693 (2003). https://doi.org/10.1067/mjd.2003.281

    Article  Google Scholar 

  16. Kittler, H., Pehamberger, H., Wolff, K., Binder, M.: Diagnostic accuracy of dermoscopy. Lancet. Oncol. 3(3), 159–165 (2002). https://doi.org/10.1016/S1470-2045(02)00679-4

    Article  Google Scholar 

  17. Silver, D., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529, 484–489 (2016). https://doi.org/10.1038/nature16961

    Article  Google Scholar 

  18. Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015). https://doi.org/10.1038/nature14236

    Article  Google Scholar 

  19. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y

    Article  MathSciNet  Google Scholar 

  20. Vestergaard, M.E., Macaskill, P., Holt, P.E., Menzies, S.W.: Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta-analysis of studies performed in a clinical setting. Br. J. Dermatol. 159(3), 669–676 (2008). https://doi.org/10.1111/j.1365-2133.2008.08713.x

    Article  Google Scholar 

  21. Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017). https://doi.org/10.1038/nature21056

    Article  Google Scholar 

  22. Rogers, H.W., Weinstock, M.A., Feldman, S.R., Coldiron, B.M.: Incidence estimate of nonmelanoma skin cancer (Keratinocyte carcinomas) in the US population. JAMA Dermatol. 151(10), 1081 (2015). https://doi.org/10.1001/jamadermatol.2015.1187

    Article  Google Scholar 

  23. Stern, R.S.: Prevalence of a history of skin cancer in 2007. Arch. Dermatol. 146(3), 279–282 (2010). https://doi.org/10.1001/archdermatol.2010.4

    Article  MathSciNet  Google Scholar 

  24. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature1539

    Article  Google Scholar 

  25. Arbib, M.A.: The Handbook of Brain Theory and Neural Networks. MIT Press (1998)

    Google Scholar 

  26. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017). https://doi.org/10.1145/3065386

    Article  Google Scholar 

  27. Liu, L., Yan, R.J., Maruvanchery, V., Kayacan, E., Chen, I.M., Tiong, L.K.: Transfer learning on convolutional activation feature as applied to a building quality assessment robot. Int. J. Adv. Robot. Syst. 14(3), 172988141771262 (2017). https://doi.org/10.1177/1729881417712620

    Article  Google Scholar 

  28. Szegedy, C., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9 (2015). https://doi.org/10.1109/CVPR.2015.7298594

  29. Ding, P., Zhang, Y., Deng, W.J., Jia, P., Kuijper, A.: A light and faster regional convolutional neural network for object detection in optical remote sensing images. ISPRS J. Photogramm. Remote Sens. 141, 208–218 (2018). https://doi.org/10.1016/j.isprsjprs.2018.05.005

    Article  Google Scholar 

  30. Masood, A., Al-Jumaily, A.A.: Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms. Int. J. Biomed. Imaging 323268 (2013). http://dx.doi.org/10.1155/2013/323268

  31. Khosla, A., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y

    Article  MathSciNet  Google Scholar 

  32. Rosado, B., et al.: Accuracy of computer diagnosis of melanoma: a quantitative meta-analysis. Arch. Dermatol. 139(3), 361–367 (2003). https://doi.org/10.1001/archderm.139.3.361

    Article  Google Scholar 

  33. Burroni, M., et al.: Melanoma computer-aided diagnosis: reliability and feasibility study. Clin. Cancer Res. 10(6), 1881–1886 (2004). https://doi.org/10.1158/1078-0432.CCR-03-0039

    Article  Google Scholar 

  34. 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.) Machine Learning in Medical Imaging. MLMI 2016. Lecture Notes in Computer Science, p. 10019 (2016). https://doi.org/10.1007/978-3-319-47157-0_20

    Chapter  Google Scholar 

  35. Milton, M.A.A.: Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks in ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Challenge (2019)

    Google Scholar 

  36. Hardie, R.C., Ali, R., De Silva, M.S., Kebede, T.M.: Skin Lesion Segmentation and Classification for ISIC 2018 Using Traditional Classifiers with Hand-Crafted Features. https://arxiv.org/abs/1807.07001

  37. Howard, A.G., et al.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017). https://arxiv.org/abs/1704.04861

  38. Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-sources dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 (2018). https://doi.org/10.1038/sdata.2018.161

    Article  Google Scholar 

  39. Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3(1), 1345–1359 (2016). https://doi.org/10.1109/TKDE.2009.191

    Article  Google Scholar 

  40. Image Preprocessing—Keras Documentation. Keras (2019). https://keras.io/preprocessing/image/. Accessed 31 Mar 2019

  41. Pandas: Working with missing data—pandas 0.22.0 documentation (2019). https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html. Accessed 31 Mar 2019

  42. Mikolajczyk, A., Grochowski, M.: Data augmentation for improving deep learning in image classification problem. In: International Interdisciplinary Ph.D. Workshop (IIPhDW), pp. 117–122 (2018). https://doi.org/10.1109/IIPHDW.2018.8388338

  43. Kaggle: Your Home for Data Science. https://www.kaggle.com/. Accessed 31 Mar 2019

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Priyadarshini Institute of Engineering & Technology, Nagpur, 440019, India

    Saket S. Chaturvedi, Kajol Gupta & Prakash S. Prasad

Authors
  1. Saket S. Chaturvedi
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  2. Kajol Gupta
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  3. Prakash S. Prasad
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Corresponding author

Correspondence to Saket S. Chaturvedi .

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Editors and Affiliations

  1. Faculty of Computer and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

    Roheet Bhatnagar

  3. Faculty of Science, Helwan University, Helwan, Egypt

    Ashraf Darwish

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Chaturvedi, S.S., Gupta, K., Prasad, P.S. (2021). Skin Lesion Analyser: An Efficient Seven-Way Multi-class Skin Cancer Classification Using MobileNet. In: Hassanien, A., Bhatnagar, R., Darwish, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2020. Advances in Intelligent Systems and Computing, vol 1141. Springer, Singapore. https://doi.org/10.1007/978-981-15-3383-9_15

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