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Ensembles of Deep Convolutional Neural Networks for Detecting Melanoma in Dermoscopy Images

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Computational Collective Intelligence (ICCCI 2021)

Abstract

Malignant melanoma is the deadliest form of skin cancer and is one of the most rapidly increasing cancers in the world. In this paper, a methodology for the SIIM-ISIC Melanoma Classification Challenge, where the goal is to detect melanoma from dermoscopic images, is described. The EfficientNet family of convolutional neural networks is utilized and extended for identifying malignant melanoma on a dataset of 58,457 dermoscopic images of pigmented skin lesions. This binary classification problem comes with a severe class imbalance, which is tackled using a loss balancing approach. Furthermore, the dataset contains images with different resolution sizes. This property is addressed by considering different model input resolutions. Lastly, an ensembling strategy of models, trained with different activation functions is applied to increase the diversity of the ensembler and to further improve individual results.

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References

  1. N. C. Institute. https://www.cancer.gov/types/common-cancers. Accessed 14 May 2021

  2. Skin Cancer Foundation. https://www.skincancer.org/skin-cancer-information/skin-cancer-facts. Accessed 14 May 2021

  3. Maglogiannis, I., Doukas, C.: Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans. Inf. Technol. Biomed. 13(5), 721–733 (2009)

    Article  Google Scholar 

  4. Siegel, R., Miller, K., Jemal, A.: Cancer statistics, 2018. CA Cancer J. Clin. 68(1), 7–30 (2018)

    Article  Google Scholar 

  5. Nami, N., Giannini, E., Burroni, M., Fimiani, M., Rubegni, P.: Teledermatology: state-of-the-art and future perspectives. Expert. Rev. Dermatol. 7, 1–3 (2012)

    Article  Google Scholar 

  6. Haenssle, H., Fink, C., Uhlmann, L.: Reply to the letter to the Editor “Reply to ‘Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists’ by H. A. Haenssle et al.” by L. Oakden-Rayner. Ann. Oncol. 30(5), 854–857 (2019)

    Article  Google Scholar 

  7. ISIC Archive. https://www.isic-archive.com. Accessed 14 May 2021

  8. Mahajan, P., Vyavahare, A.: Artefact removal and contrast enhancement for dermoscopic images using image processing techniques. Int. J. Innov. Res. Electric. Electron. Instrum. Control Eng. 1, 418–421 (2013)

    Google Scholar 

  9. Bakheet, S.: An SVM framework for malignant melanoma detection based on optimized HOG features. Computation 5, 4 (2017)

    Article  Google Scholar 

  10. Maragoudakis, M., Maglogiannis, I.: A medical ontology for intelligent web-based skin lesions image retrieval. Health Inform. J. 17(2), 140–157 (2011)

    Article  Google Scholar 

  11. Abbas, Q., Emre Celebi, M., Fondón, I.: Computer-aided pattern classification system for dermoscopy images. Skin Res. Technol. 18(3), 278–289 (2011)

    Article  Google Scholar 

  12. Stoecker, W., Li, W., Moss, R.: Automatic detection of asymmetry in skin tumors. Comput. Med. Imaging Graph. 16(3), 191–197 (1992)

    Article  Google Scholar 

  13. Celebi, M., Zornberg, A.: Automated quantification of clinically significant colors in dermoscopy images and its application to skin lesion classification. IEEE Syst. J. 8(3), 980–984 (2014)

    Article  Google Scholar 

  14. Stanley, R., Stoecker, W., Moss, R.: A relative color approach to color discrimination for malignant melanoma detection in dermoscopy images. Skin Res. Technol. 13(1), 62–72 (2007)

    Article  Google Scholar 

  15. Doukas, C., Stagkopoulos, P., Maglogiannis, I.: Skin lesions image analysis utilizing smartphones and cloud platforms. Methods Mol. Biol. 1256, 435–458 (2015)

    Article  Google Scholar 

  16. Delibasis, K., Kotari, K., Maglogiannis, I.: Automated detection of streaks in dermoscopy images. IFIP Adv. Inf. Commun. Technol. 458, 45–60 (2015)

    Article  Google Scholar 

  17. Iyatomi, H., et al.: Computer-based classification of dermoscopy images of melanocytic lesions on acral volar skin. J. Invest. Dermatol. 128(8), 2049–2054 (2008)

    Article  Google Scholar 

  18. Maglogiannis, I., Delibasis, K.: Enhancing classification accuracy utilizing globules and dots features in digital dermoscopy. Comput. Methods Programs Biomed. 118(2), 124–133 (2015)

    Article  Google Scholar 

  19. Maglogiannis, I., Kosmopoulos, D.: Computational vision systems for the detection of malignant melanoma. Oncol. Rep. 15(4), 1027–1032 (2006)

    Google Scholar 

  20. Li, L., et al.: Automatic diagnosis of melanoma using machine learning methods on a spectroscopic system. BMC Med. Imaging 14, 36 (2014)

    Article  Google Scholar 

  21. Victor, A., Ghalib, M.: Automatic detection and classification of skin cancer. Int. J. Intell. Eng. Syst. 10(3), 444–451 (2017)

    Google Scholar 

  22. Kontogianni, G., Maglogiannis, I.: A review on state-of-the-art computer-based approaches for the early recognition of malignant melanoma. Stud. Comput. Intell. 891, 81–101 (2020)

    Google Scholar 

  23. Georgakopoulos, S.V., Kottari, K., Delibasis, K., Plagianakos, V.P., Maglogiannis, I.: Detection of malignant melanomas in dermoscopic images using convolutional neural network with transfer learning. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds.) EANN 2017. CCIS, vol. 744, pp. 404–414. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65172-9_34

    Chapter  Google Scholar 

  24. Georgakopoulos, S.V., Kottari, K., Delibasis, K., Plagianakos, V.P., Maglogiannis, I.: Improving the performance of convolutional neural network for skin image classification using the response of image analysis filters. Neural Comput. Appl. 31(6), 1805–1822 (2018). https://doi.org/10.1007/s00521-018-3711-y

    Article  Google Scholar 

  25. Gessert, N., et al.: Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data. MethodsX 7(7), 100864 (2020)

    Article  Google Scholar 

  26. Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)

    Article  Google Scholar 

  27. Kawahara, J., BenTaieb, A., Hamarneh, G.: Deep features to classify skin lesions. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1397–1400 (2016)

    Google Scholar 

  28. Liao, H.: A Deep Learning Approach to Universal Skin Disease Classification (2015)

    Google Scholar 

  29. Li, Y., Shen, L.: Skin lesion analysis towards melanoma detection using deep learning network. Sensors 18(2), 556 (2018)

    Article  Google Scholar 

  30. Rezvantalab, A., Safigholi, H., Karimijeshni, S.: Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms. arXiv:1810.10348 (2018)

  31. Codella, N.C.F., et al.: Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J. Res. Dev. 61(4–5), 1–15 (2017)

    Google Scholar 

  32. 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)

    Google Scholar 

  33. Codella, N.C.F., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 International symposium on biomedical imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC). arXiv:1710.05006 (2018)

  34. Combalia, M., et al.: BCN20000: Dermoscopic Lesions in the Wild. arXiv:1908.02288 (2019)

  35. Rotemberg, V., et al.: A patient-centric dataset of images and metadata for identifying melanomas using clinical context. Sci. Data 8(1), 34 (2021)

    Article  Google Scholar 

  36. Tan, M., Le, Q.V.: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv:1905.11946 (2019)

  37. Misra, D.: Mish: A Self Regularized Non-Monotonic Activation Function. arXiv:1908.08681 (2020)

  38. Ho, Y., Wookey, S.: The real-world-weight cross-entropy loss function: modeling the costs of mislabeling. IEEE Access 8, 4806–4813 (2020)

    Article  Google Scholar 

  39. Smith, L.N.: Cyclical learning rates for training neural networks. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 464–472 (2017)

    Google Scholar 

  40. Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: Proceedings of the 34th International Conference on Machine Learning, pp. 3319–3328 (2017)

    Google Scholar 

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Acknowledgment

This research has been co‐financed by the EU and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: Transition - T1EDK-01385).

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Correspondence to Melina Tziomaka .

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Tziomaka, M., Maglogiannis, I. (2021). Ensembles of Deep Convolutional Neural Networks for Detecting Melanoma in Dermoscopy Images. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_39

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  • DOI: https://doi.org/10.1007/978-3-030-88081-1_39

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