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Evolving Feature Extraction Models for Melanoma Detection: A Co-operative Co-evolution Approach

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Applications of Evolutionary Computation (EvoApplications 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14634))

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Abstract

As global mortality rates rise alongside an increasing incidence of skin cancer, it becomes increasingly clear that the pursuit of an effective strategy to combat this challenge is gaining urgency. In traditional practices, the diagnosis of skin cancer predominantly depends on manual inspection of skin lesions. Despite its prevalent use, this approach is beset with several limitations, such as subjectivity, time constraints, and the invasive nature of biopsy procedures. Addressing these obstacles, the burgeoning field of Artificial Intelligence has been instrumental in advancing Computer Automated Diagnostic Systems (CADS) for skin cancer. A critical aspect of these systems is feature extraction, a process crucial for discerning and utilising key characteristics from raw image data, thereby bolstering the efficacy of CADS. This study introduces a feature extraction model that evolves automatically, leveraging the principles of genetic programming and cooperative coevolution. This method generates a ensemble of models that collaboratively work to extract discerning features from images of skin lesions. The model’s effectiveness is evaluated using a publicly accessible dataset, whilst further analysis pertaining to interactions between the decomposition of image colour channels are explored. The findings indicate that the proposed method either matches or significantly surpasses the performance of established benchmarks and recent methodologies in this field, underscoring its potential in enhancing skin cancer diagnostic processes.

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References

  1. Sung,H., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer Clin. 71, 209–49 (2021). This report provides the latest global cancer statistics of incidence and mortality worldwide, 2022

    Google Scholar 

  2. Pehamberger, H., Steiner, A., Wolff, K.: In vivo epiluminescence microscopy of pigmented skin lesions. I. Pattern analysis of pigmented skin lesions. J. Am. Acad. Dermatol. 17(4), 571–583 (1987)

    Article  Google Scholar 

  3. Argenziano, G., Fabbrocini, G., Carli, P., De Giorgi, V., Sammarco, E., Delfino, M.: Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions: comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Arch. Dermatol. 134(12), 1563–1570 (1998)

    Article  Google Scholar 

  4. Menzies, S.W., Crotty, K., Ingvar, C., McCarthy, W.: Dermoscopy: An Atlas, 3rd edn. McGraw-Hill Education, Australia (2009)

    Google Scholar 

  5. Henning, J.S., et al.: The CASH (color, architecture, symmetry, and homogeneity) algorithm for dermoscopy. J. Am. Acad. Dermatol. 56(1), 45–52 (2007)

    Article  Google Scholar 

  6. Stolz, W.: ABCD rule of dermatoscopy: a new practical method for early recognition of malignant melanoma. Eur. J. Dermatol. 4, 521–527 (1994)

    Google Scholar 

  7. Loescher, L.J., Janda, M., Soyer, H.P., Shea, K., Curiel-Lewandrowski, C.: Advances in skin cancer early detection and diagnosis. In: Proceedings of Seminars in Oncology Nursing, vol. 29, pp. 170–181. Elsevier (2013)

    Google Scholar 

  8. Carrera, C., et al.: Validity and reliability of dermoscopic criteria used to differentiate nevi from melanoma: a web-based international dermoscopy society study. JAMA Dermatol. 152(7), 798–806 (2016)

    Article  Google Scholar 

  9. Resneck, J., Jr., Pletcher, M.J., Lozano, N.: Medicare, medicaid, and access to dermatologists: the effect of patient insurance on appointment access and wait times. J. Am. Acad. Dermatol. 50(1), 85–92 (2004)

    Article  Google Scholar 

  10. Bichakjian, C.K., et al.: Guidelines of care for the management of primary cutaneous melanoma. J. Am. Acad. Dermatol. 65(5), 1032–1047 (2011)

    Article  Google Scholar 

  11. Schadendorf, D., et al.: Melanoma. The Lancet 392(10151), 971–984 (2018)

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press (1992)

    Google Scholar 

  14. Liang, J., Wen, J., Wang, Z., Wang, J.: Evolving semantic object segmentation methods automatically by genetic programming from images and image processing operators. Soft. Comput. 24, 12887–12900 (2020)

    Article  Google Scholar 

  15. Cano, A., Ventura, S., Cios, K.J.: Multi-objective genetic programming for feature extraction and data visualization. Soft. Comput. 21, 2069–2089 (2017)

    Article  Google Scholar 

  16. Potter, M.A., De Jong, K.A.: A cooperative coevolutionary approach to function optimization. In: Davidor, Y., Schwefel, H.-P., Männer, R. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994). https://doi.org/10.1007/3-540-58484-6_269

    Chapter  Google Scholar 

  17. Ain, Q.U., Xue, B., Al-Sahaf, H., Zhang, M.: Genetic programming for skin cancer detection in dermoscopic images. In: Proceedings of the 2017 IEEE Congress on Evolutionary Computation, pp. 2420–2427. IEEE (2017)

    Google Scholar 

  18. Ain, Q.U., Al-Sahaf, H., Xue, B., Zhang, M.: Automatically diagnosing skin cancers from multimodality images using two-stage genetic programming. IEEE Trans. Cybern. 53(5), 2727–2740 (2022)

    Article  Google Scholar 

  19. Al-Sahaf, H., Zhang, M., Johnston, M., Verma, B.: Image descriptor: a genetic programming approach to multiclass texture classification. In: Proceedings of the 2015 IEEE Congress on Evolutionary Computation, pp. 2460–2467. IEEE (2015)

    Google Scholar 

  20. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  21. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)

    Google Scholar 

  22. Codella, N., Cai, J., Abedini, M., Garnavi, R., Halpern, A., Smith, J.R.: Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds.) MLMI 2015. LNCS, vol. 9352, pp. 118–126. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24888-2_15

    Chapter  Google Scholar 

  23. Barata, C., Celebi, M.E., Marques, J.S.: A survey of feature extraction in dermoscopy image analysis of skin cancer. IEEE J. Biomed. Health Inf. 23(3), 1096–1109 (2018)

    Article  Google Scholar 

  24. Codella, N.C., 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). In: Proceedings of the 15th International Symposium on Biomedical Imaging, pp. 168–172. IEEE (2018)

    Google Scholar 

  25. Ain, Q.U., Al-Sahaf, H., Xue, B., Zhang, M.: Automatically diagnosing skin cancers from multimodality images using two-stage genetic programming. IEEE Trans. Cybern. 53, 2727–2740 (2022)

    Article  Google Scholar 

  26. Barata, C., Ruela, M., Francisco, M., Mendonça, T., Marques, J.S.: Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Syst. J. 8(3), 965–979 (2013)

    Article  Google Scholar 

  27. Barata, C., Celebi, M.E., Marques, J.S.: Improving dermoscopy image classification using color constancy. IEEE J. Biomed. Health Inform. 19(3), 1146–1152 (2014)

    Google Scholar 

  28. Al-Sahaf, H., Zhang, M., Johnston, M.: Genetic programming for multiclass texture classification using a small number of instances. In: Dick, G., et al. (eds.) SEAL 2014. LNCS, vol. 8886, pp. 335–346. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13563-2_29

    Chapter  Google Scholar 

  29. Al-Sahaf, H., Al-Sahaf, A., Xue, B., Johnston, M., Zhang, M.: Automatically evolving rotation-invariant texture image descriptors by genetic programming. IEEE Trans. Evol. Comput. 21(1), 83–101 (2017)

    Google Scholar 

  30. Mendonça, T., Ferreira, P.M., Marques, J.S., Marcal, A.R., Rozeira, J.: PH\(^2\) - a dermoscopic image database for research and benchmarking. In: Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5437–5440. IEEE (2013)

    Google Scholar 

  31. Ain, Q.U., Al-Sahaf, H., Xue, B., Zhang, M.: Generating knowledge-guided discriminative features using genetic programming for melanoma detection. IEEE Trans. Emerg. Top. Computat. Intell. 5(4), 554–569 (2020)

    Article  Google Scholar 

  32. Ain, Q.U., Al-Sahaf, H., Xue, B., Zhang, M.: A new genetic programming representation for feature learning in skin cancer detection. In: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, pp. 707–710 (2023)

    Google Scholar 

  33. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, P.O. Box 600, Wellington, 6140, New Zealand

    Taran Cyriac John, Qurrat Ul Ain, Harith Al-Sahaf & Mengjie Zhang

Authors
  1. Taran Cyriac John
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  2. Qurrat Ul Ain
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  3. Harith Al-Sahaf
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  4. Mengjie Zhang
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Correspondence to Taran Cyriac John .

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Editors and Affiliations

  1. University of York, York, UK

    Stephen Smith

  2. University of Coimbra, Coimbra, Portugal

    João Correia

  3. University of Málaga, Málaga, Spain

    Christian Cintrano

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John, T.C., Ain, Q.U., Al-Sahaf, H., Zhang, M. (2024). Evolving Feature Extraction Models for Melanoma Detection: A Co-operative Co-evolution Approach. In: Smith, S., Correia, J., Cintrano, C. (eds) Applications of Evolutionary Computation. EvoApplications 2024. Lecture Notes in Computer Science, vol 14634. Springer, Cham. https://doi.org/10.1007/978-3-031-56852-7_26

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  • DOI: https://doi.org/10.1007/978-3-031-56852-7_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56851-0

  • Online ISBN: 978-3-031-56852-7

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