Bio-Inspired Feed-Forward System for Skin Lesion Analysis, Screening and Follow-Up

  • Francesco RundoEmail author
  • Sabrina Conoci
  • Giuseppe L. Banna
  • Filippo Stanco
  • Sebastiano BattiatoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10485)


Traditional methods for early detection of melanoma rely upon a dermatologist who visually analyzes skin lesion using the so called ABCDE (Asymmetry, Border irregularity, Color variegation, Diameter, Evolution) criteria even though conclusive confirmation is obtained through biopsy performed by pathologist. The proposed method shows a bio-inspired feed-forward automatic pipeline based on morphological analysis and evaluation of skin lesion dermoscopy image. Preliminary segmentation and pre-processing of dermoscopy image by SC-Cellular Neural Networks is performed in order to get ad-hoc gray-level skin lesion image in which we compute analytic innovative hand-crafted image features for oncological risks assessment. At the end, pre-trained Levenberg-Marquardt Neural Network is used to perform ad-hoc clustering of such hand-crafted image features in order to get an efficient nevus discrimination (benign against melanoma) as well as a numerical array to be used for follow-up rate definition and assessment.


  1. 1.
    Rundo, F., Banna, G.L.: A method of analyzing skin lesions, corresponding system, instrument and computer program product. EU Registered Patent App. N. 102016000121060, 29 November 2016Google Scholar
  2. 2.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall, Upper Saddle River (2007)Google Scholar
  3. 3.
    Barata, C., Ruela, M., et al.: Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Syst. J. 99, 1–15 (2013)Google Scholar
  4. 4.
    Celebi, M.E., Wen, Q., Hwang, S., Iyatomi, H., Schaefer, G.: Lesion border detection in dermoscopy images using ensembles of thresholding methods. Skin Res. Technol. 19(1), e252–e258 (2013)CrossRefGoogle Scholar
  5. 5.
    Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 93–202 (1980)CrossRefzbMATHGoogle Scholar
  6. 6.
    Fridan, U., et al.: Classification of skin lesions using ANN. Medical Technologies National Congress (TIPTEKNO) (2016)Google Scholar
  7. 7.
    Xie, F., Fan, F., et al.: Melanoma classification on dermoscopy images using a neural network ensemble model. IEEE Trans. Med. Imaging 36(3), 849–858 (2016)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Battiato, S., Rundo, F., Stanco, F.: ALZ: adaptive learning for zooming digital image. In: IEEE Proceedings of International Conference on Consumer and Electronics (2007)Google Scholar
  9. 9.
    Binu Sathiya, S., Kumar, S.S., Prabin, A.: A survey on recent computer-aided diagnosis of Melanoma. In: 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) (2014)Google Scholar
  10. 10.
    Mendonça, T., Ferreira, P.M., et al.: PH2 - a dermoscopic image database for research and benchmarking. In: 35th International Conference of the IEEE Engineering in Medicine and Biology Society, 3–7 July 2013, Osaka, Japan (2013)Google Scholar
  11. 11.
    Majtner, T., Yildirim-Yayilgan, S., Hardeberg, J.Y.: Combining deep learning and hand-crafted features for skin lesion classification. In: 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA) (2016)Google Scholar
  12. 12.
    Jamil, U., Khalid, S., Usman Akram, M.: Dermoscopic feature analysis for melanoma recognition and prevention. In: 2016 Sixth International Conference on Innovative Computing Technology (INTECH) (2016)Google Scholar
  13. 13.
    Rashad, M.W., Takruri, M.: Automatic non-invasive recognition of melanoma using Support Vector Machines. In: 2016 BioSMART Conference (2016)Google Scholar
  14. 14.
    Chua, L.O., Yang, L.: Cellular neural networks: theory. IEEE Trans. Circ. Syst. 35(10), 1257–1272 (1988)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Arena, P., Baglio, S., Fortuna, L., Manganaro, G.: Dynamics of state controlled CNNs. In: IEEE Proceedings of International Symposium on Circuits and Systems, ISCAS 1996 (1996)Google Scholar
  16. 16.
    Hagan, M.T., Menhaj, M.: Training feed-forward networks with Marquardt algorithm. IEEE Trans. Neural Netw. 5(6), 989–993 (1994)CrossRefGoogle Scholar
  17. 17.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.ADG Central R&DSTMicroelectronicsCataniaItaly
  2. 2.Medical Oncology DepartmentCannizzaro Medical HospitalCataniaItaly
  3. 3.DMI IPLABUniversity of CataniaCataniaItaly

Personalised recommendations