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Fuzzy decision ontology for melanoma diagnosis using KNN classifier

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Abstract

Melanoma is the most dangerous type of skin cancer when discovered in an advanced stage. Early detection of melanoma improves survival. Several Computer -Aided Diagnosis (CAD) systems are currently developed to speed up early diagnosis. Recently, ontology is widely adapted for describing and diagnosing a disease. For melanoma detection, the ontology reasoning of dermatologists is based on expert rules, such as ABCD rule. Accordingly, dermatologists classify skin lesions in three classes: melanoma, benign, and recommended follow-up class. In this paper, we propose a CAD system based on an ontology for melanoma diagnosis by giving the probability of being melanoma. We first present our ontology focusing on its main concepts involved in ABCD rule: Asymmetry, Border, Color and Differential structures. Accordingly, the Bag-of-Words, modeling these concepts, are generated from extracted features of skin lesion images. An important step in ontology is to define rules relating the different concepts. In our case, these rules allow the fuzzy decision to classify lesion in melanoma, benign or recommended follow-up class with a malignancy probability. Considering the similarity of melanoma cases, the K-Nearest Neighbors approach is applied to make the final decision in case of a recommended follow-up class. Experimental validation on two public datasets of 206 lesion images shows that our approach presents an efficient method of analysis and can be more appropriate for lesion severity classification. It yields a sensitivity of (96%) and an accuracy of (92%), surpassing existing recent approaches on melanoma diagnosis.

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References

  1. AAD (2019) American academy of dermatology Accessed at web.archive.org/web/20190801005313/ https://www.aad.org/public/diseases/skin-cancer/melanoma

  2. Abbes W, Sellami D (2016) High-level features for automatic skin lesions neural network based classification. Conference image processing, applications and systems (IPAS), pp 1–7

  3. Abbes W, Sellami D (2017) Automatic skin lesions classification using ontology-based semantic analysis of optical standard images. Procedia Comput Sci 112:2096–2105

    Article  Google Scholar 

  4. ACS (2019) Cancer facts and figures 2019, american cancer society. Accessed at web.archive.org/web/ 20191008163453/ https://cancerstatisticscenter.cancer.org/?_ga=2.60550250.6525233021550525710-937655358.1545427513/

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

    Article  Google Scholar 

  6. Argenziano G, Soyer H, De Giorgi V, Piccolo D, Carli P, Delfino M et al (2002) Dermoscopy: a tutorial edra

  7. Braun RP, Rabinovitz HS, Oliviero M, Kopf AW, Saurat J-H (2005) Dermoscopy of pigmented skin lesions. J Am Acad Dermatol 52(1):109–121

    Article  Google Scholar 

  8. Celebi M, Zornberg A (2014) Automated quantification of clinically significant colors in dermoscopy images and its application to skin lesionclassification. IEEE Sys J 8(3):980–4

    Article  Google Scholar 

  9. Chang C-C, Hsiao J-Y, Hsieh C-P (2008) An adaptive median filter for image denoising. In: Second international symposium on intelligent information technology application, 2008. IITA’08, vol 2. IEEE, pp 346–350

  10. Codella NC, Nguyen Q-B, Pankanti S, Gutman D, Helba B, Halpern A, Smith JR (2017) Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J Res Dev 61(4):5–1

    Google Scholar 

  11. Cokkinides V, Albano J, Samuels A, Ward M, Thum J (2005) American cancer society: Cancer facts and figures. American Cancer Society, Atlanta

    Google Scholar 

  12. DermQuest (2012) Dermquest, www.dermquest.com. from University of California, San Francisco, UCSF

  13. DIS (2012) Dermatology information system, www.dermis.net. from DermIs.net

  14. Engasser H, Warshaw E (2010) Dermatoscopy use by us dermatologists: a cross-sectional survey. J Am Acad Dermatol 63(3):412–9

    Article  Google Scholar 

  15. Fan J, Zhou N, Peng J, Gao L (2015) Hierarchical learning of tree classifiers for large-scale plant species identification. IEEE Trans Image Process 24 (11):4172–4184

    Article  MathSciNet  Google Scholar 

  16. Gruber T (1993) A translation approach to portable ontologies. Knowl Acquisit 5(2):199–220

    Article  Google Scholar 

  17. Gupta S, Szekely P, Knoblock C, Goel A, Taheriyan M, Muslea M (2012) Karma: a system for mapping structured sources into the semantic web. Extended Semantic Web Conference, pp 430–434

  18. Haralick R, Shanmugam K (1973) Textural features for image classification. IEEE Trans Sys Man Cybern 3(6):610–21

    Article  Google Scholar 

  19. Horrocks I, Patel-Schneider PF, Boley H, Tabet S, Grosof B, Dean M et al (2004) Swrl: a semantic web rule language combining owl and ruleml. W3C Member submission, 21(79)

  20. Huang CL, Halpern AC (2005) Management of the patient with melanoma. Cancer of the Skin, pp 265–275

  21. Kaliyadan F, Ashique K, Jagadeesan S (2018) A survey on the pattern of dermoscopy use among dermatologists in India. Indian J Dermatol Venereol Leprol 84(1):120

    Article  Google Scholar 

  22. Kuo Y, Chang Y, Wang S, Lu P, Su Y, Chu T, Chu G (2015) Survey of dermoscopy use by taiwanese dermatologists. DermatologicaSinica 33 (4):215–219

    Google Scholar 

  23. Lopez AR, Giro-i Nieto X, Burdick J, Marques O (2017) Skin lesion classification from dermoscopic images using deep learning techniques. In: 2017 13th IASTED international conference on biomedical engineering (BioMed). IEEE, pp 49–54

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

    Article  Google Scholar 

  25. Maragoudakis M, Maglogiannis I, Lymberopoulos D (2008) A medical, description logic based, ontology for skin lesion images. In: 2008 8th IEEE international conference on bioinformatics and bioengineering. IEEE, pp 1–6

  26. Marchetti MA, Codella NC, Dusza SW, Gutman DA, Helba B, Kalloo A, Mishra N, Carrera C, Celebi ME, DeFazio JL et al (2018) Results of the 2016 international skin imaging collaboration international symposium on biomedical imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. J Am Acad Dermatol 78(2):270–277

    Article  Google Scholar 

  27. Muir B (1987) Trust between humans and machines, and the design of decision aids. Int J Man-Machine Stud 27(5-6):527–539

    Article  Google Scholar 

  28. Murugan A, Nair SAH, Kumar KS (2019) Detection of skin cancer using svm, random forest and knn classifiers. J Med Sys 43(8):269

    Article  Google Scholar 

  29. Quang NH et al (2017) Automatic skin lesion analysis towards melanoma detection. In: 2017 21st Asia Pacific symposium on intelligent and evolutionary systems (IES). IEEE, pp 106–111

  30. Rijsbergen CJV (1979) Information retrieval. In: Information retrieval. IEEE, 2nd Butterworth-Heinemann Newton, MA, USA

  31. Sheha M, Mabrouk M, Sharawy A (2012) Automatic detection of melanoma skin cancer using texture analysis. Int J Comput Appl 42(20):22–26

    Google Scholar 

  32. Shen J, Deng RH, Cheng Z, Nie L, Yan S (2015) On robust image spam filtering via comprehensive visual modeling. Pattern Recogn 48(10):3227–3238

    Article  Google Scholar 

  33. Sherimon P, Krishnan R (2016) Ontodiabetic: an ontology-based clinical decision support system for diabetic patients. Arab J Sci Eng 41(3):1145–1160

    Article  Google Scholar 

  34. Steiner A, Binder M, Schemper M, Wolff K, Pehamberger H (1993) Statistical evaluation of epiluminescence microscopy criteria for melanocytic pigmented skin lesions. J Am Acad Dermatol 29(4):581–588

    Article  Google Scholar 

  35. Wang L, Qian X, Zhang Y, Shen J, Cao X (2019) Enhancing sketch-based image retrieval by cnn semantic re-ranking. IEEE Trans Cybern 50 (7):3330–3342

    Article  Google Scholar 

  36. Yu L, Chen H, Dou Q, Qin J, Heng P-A (2017) Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans Med Imag 36(4):994–1004

    Article  Google Scholar 

  37. Zhao T, Zhang B, He M, Zhang W, Zhou N, Yu J, Fan J (2018) Embedding visual hierarchy with deep networks for large-scale visual recognition. IEEE Trans Image Process 27(10):4740–4755

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. CEM Laboratory, National Engineering School of Sfax, Sfax University, Soukra street, Sfax, 3038, Tunisia

    Wiem Abbes & Dorra Sellami

  2. ICUBE/SDC Team, University of Strasbourg, CNRS, France

    Stella Marc-Zwecker

  3. Normandy University, INSA Rouen, LITIS/MIND, 76000, Saint-Étienne-du-Rouvray, France

    Cecilia Zanni-Merk

Authors
  1. Wiem Abbes
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  2. Dorra Sellami
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  3. Stella Marc-Zwecker
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  4. Cecilia Zanni-Merk
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Correspondence to Wiem Abbes.

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Abbes, W., Sellami, D., Marc-Zwecker, S. et al. Fuzzy decision ontology for melanoma diagnosis using KNN classifier. Multimed Tools Appl 80, 25517–25538 (2021). https://doi.org/10.1007/s11042-021-10858-4

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  • DOI: https://doi.org/10.1007/s11042-021-10858-4

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