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Intelligent Data Engineering and Analytics pp 701–710Cite as

Image Assisted Assessment of Cancer Segment from Dermoscopy Images

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1177))

Abstract

Previous research confirms the accessibility of an assortment of picture assessment schemes for the skin melanoma appraisal. Skin Melanoma (SM) is also one of the deadliest diseases, and the unnoticed SM may direct to casualty. In this study, the SM image assessment is based on the Bat Algorithm (BA) and Kapur’s threshold. Active Contour Segmentation (ACS) is employed to mine and study the melanoma tainted skin fragment. In this work, the dermoscopy images of the benchmark data, like DermIS and Dermquest are considered for the inspection. Primarily, all the pictures are transformed into 256 × 256 pixels together with the ground truth (GT) slice, and these pictures are then used in the inspection. The efficacy of the projected procedure is then validated using a comparative examination among the mined skin segment and GT. The experimental result substantiates that this procedure helps to achieve a better Jaccard-Index and Dice value for the considered datasets; hence this procedure is appropriate to inspect the SM pictures.

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References

  1. Bhandary, A., et al.: Deep-learning framework to detect lung abnormality—a study with chest X-ray and lung CT scan images. Pattern Recogn. Lett. (2019). https://doi.org/10.1016/j.patrec.2019.11.013

    Article  Google Scholar 

  2. Fernandes, S.L., et al.: A reliable framework for accurate brain image examination and treatment planning based on early diagnosis support for clinicians. Neural Comput. Appl. 1–12 (2019). https://doi.org/10.1007/s00521-019-04369-5

  3. Dey, N., et al.: Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality. Biocybern. Biomed. Eng. 39(3), 843–856 (2019). https://doi.org/10.1016/j.bbe.2019.07.005

    Article  Google Scholar 

  4. Acharya, U.R., et al.: Automated detection of Alzheimer’s disease using brain MRI images—a study with various feature extraction techniques. J. Med. Syst. 43, 302 (2019). https://doi.org/10.1007/s10916-019-1428-9

    Article  Google Scholar 

  5. Jahmunah, V., et al.: Automated detection of schizophrenia using nonlinear signal processing methods. Artif. Intell. Med. 100, 101698 (2019). https://doi.org/10.1016/j.artmed.2019.07.006

    Article  Google Scholar 

  6. Bhateja, V., Nigam, M., Bhadauria, A.S., Arya, A., Yu-Dong Zhang, Y-D.: Human visual system based optimized mathematical morphology approach for enhancement of brain MR images. J. Ambient. Intell. Humaniz. Comput. 1–9 (2019). https://doi.org/10.1007/s12652-019-01386-z

  7. Satapathy, S.C., El-Maleh, A., Bhateja, V.: Intelligent computing in multidisciplinary engineering applications. Arab. J. Sci. Eng. 43(8), 3861–3862 (2018)

    Article  Google Scholar 

  8. Bhateja, V., Misra, M., Urooj, S.: Unsharp masking approaches for HVS based enhancement of mammographic masses: a comparative evaluation. Futur. Gener. Comput. Syst. 82, 176–189 (2018)

    Article  Google Scholar 

  9. Wang, R., Wang, G.: Web text categorization based on statistical merging algorithm in big data environment. Int. J. Ambient Comput. Intell. (IJACI) 10(3), 17–32 (2019). https://doi.org/10.4018/IJACI.2019070102

    Article  Google Scholar 

  10. Ali, et al.: Adam deep learning with SOM for human sentiment classification. Int. J. Ambient Comput. Intell. (IJACI) 10(3), 92–116 (2019). https://doi.org/10.4018/IJACI.2019070106

    Article  Google Scholar 

  11. Rajinikanth, V., Raja, N.S.M., Arunmozhi, S.: ABCD rule implementation for the skin melanoma assesment—a study. In: IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1–4. IEEE (2019). https://doi.org/10.1109/icscan.2019.8878860

  12. Amelard, R., Glaister, J., Wong, A., Clausi, D.A.: Melanoma decision support using lighting-corrected intuitive feature models. In: Computer Vision Techniques for the Diagnosis of Skin Cancer, Series in BioEngineering, pp. 193–219 (2013)

    Google Scholar 

  13. Nachbar, F., Stolz, W., Merckle, T., et al.: The ABCD rule of dermatoscopy: High prospective value in the diagnosis of doubtful melanocytic skin lesions. J. Am. Acad. Dermatol. 30, 551–559 (1994)

    Article  Google Scholar 

  14. Dey, N., Rajinikanth, V., Ashour, A.S., Tavares, J.M.R.S.: Social group optimization supported segmentation and evaluation of skin melanoma images. Symmetry 10(2), 51 (2018). https://doi.org/10.3390/sym10020051

    Article  MATH  Google Scholar 

  15. http://vip.uwaterloo.ca/demos/skin-cancer-detection

  16. Amelard, R., Glaister, J.: Wong, A. and Clausi, D.A.: High-level intuitive features (HLIFs) for intuitive skin lesion description. IEEE Trans. Biomed. Eng. 62(3), 820–831 (2015)

    Google Scholar 

  17. Satapathy, S.C., Raja, N.S.M., Rajinikanth, V., Ashour, A.S.: Dey, N: Multi-level image thresholding using Otsu and chaotic bat algorithm. Neural Comput. Appl. (2016). https://doi.org/10.1007/s00521-016-2645-5

    Article  Google Scholar 

  18. Yang, X.S.: Nature-Inspired Metaheuristic Algorithms, 2nd edn. Luniver Press, Frome (2011)

    Google Scholar 

  19. Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 29, 273–285 (1985)

    Article  Google Scholar 

  20. Yang, X., Jiang, X.: A hybrid active contour model based on new edge-stop functions for image segmentation. Int. J. Ambient Comput. Intell. (IJACI) 11(1), 87–98 (2020). https://doi.org/10.4018/IJACI.2020010105

    Article  Google Scholar 

  21. Satapathy, S.C., Rajinikanth, V.: Jaya algorithm guided procedure to segment tumor from brain MRI. J. Optim. 2018, 12 (2018). https://doi.org/10.1155/2018/3738049

    Article  MATH  Google Scholar 

  22. Fernandes, S.L., Rajinikanth, V., Kadry, S.: A hybrid framework to evaluate breast abnormality using infrared thermal images. IEEE Consum. Electron. Mag. 8(5), 31–36 (2019). https://doi.org/10.1109/MCE.2019.2923926

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronics and Instrumentation, St. Joseph’s College of Engineering, Chennai, 600119, Tamil Nadu, India

    M. Santhosh, R. Rubin Silas Raj & V. Rajinikanth

  2. School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to Be University), Bhubaneswar, 751024, Odisha, India

    Suresh Chandra Satapathy

Authors
  1. M. Santhosh
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  2. R. Rubin Silas Raj
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  3. V. Rajinikanth
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  4. Suresh Chandra Satapathy
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Corresponding author

Correspondence to V. Rajinikanth .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute Industrial Technology, Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  2. Department of Informatics, University of Leicester, Leicester, UK

    Yu-Dong Zhang

  3. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  4. School of Management, National Institute of Technology Karnataka, Surathkal, Karnataka, India

    Ritanjali Majhi

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Santhosh, M., Rubin Silas Raj, R., Rajinikanth, V., Satapathy, S.C. (2021). Image Assisted Assessment of Cancer Segment from Dermoscopy Images. In: Satapathy, S., Zhang, YD., Bhateja, V., Majhi, R. (eds) Intelligent Data Engineering and Analytics. Advances in Intelligent Systems and Computing, vol 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-5679-1_68

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