Your browser does not support JavaScript!

Evaluation of Levenberg–Marquardt neural networks and stacked autoencoders clustering for skin lesion analysis, screening and follow-up

Evaluation of Levenberg–Marquardt neural networks and stacked autoencoders clustering for skin lesion analysis, screening and follow-up

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Traditional methods for early detection of melanoma rely on the visual analysis of the skin lesions performed by a dermatologist. The analysis is based on the so-called ABCDE (Asymmetry, Border irregularity, Colour variegation, Diameter, Evolution) criteria, although confirmation is obtained through biopsy performed by a pathologist. The proposed method exploits an automatic pipeline based on morphological analysis and evaluation of skin lesion dermoscopy images. Preliminary segmentation and pre-processing of dermoscopy image by SC-cellular neural networks is performed, in order to obtain ad-hoc grey-level skin lesion image that is further exploited to extract analytic innovative hand-crafted image features for oncological risks assessment. In the end, a pre-trained Levenberg–Marquardt neural network is used to perform ad-hoc clustering of such features in order to achieve an efficient nevus discrimination (benign against melanoma), as well as a numerical array to be used for follow-up rate definition and assessment. Moreover, the authors further evaluated a combination of stacked autoencoders in lieu of the Levenberg–Marquardt neural network for the clustering step.


    1. 1)
      • 19. Titsias, M.K.: ‘Variational learning of inducing variables in sparse Gaussian processes’. Int. Conf. Artificial Intelligence and Statistics, 2009a, vol. 12, pp. 567574.
    2. 2)
      • 14. Fukushima, K.: ‘Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position’, Biol. Cybern., 1980, 36, (4), pp. 93202.
    3. 3)
      • 15. Fridan, U., Sarı, İ., Kumrular, R.K.: ‘Classification of skin lesions using ANNMedical Technologies National Congress (TIPTEKNO), Antalya, Turkey, 2016.
    4. 4)
      • 6. Rashad, M.W., Takruri, M.: ‘Automatic non-invasive recognition of melanoma using support vector machines’. BioSMART Conf., 2016.
    5. 5)
      • 20. Barata, C., Ruela, M., Francisco, M., et al: ‘Two systems for the detection of melanomas in dermoscopy images using texture and color features’, IEEE Syst. J., 2013, 99, pp. 115.
    6. 6)
      • 21. STM32 32-bit ARM Cortex MCUs: Available at, accessed August 2018.
    7. 7)
      • 13. Hagan, M.T., Menhaj, M.: ‘Training feed-forward networks with Marquardt algorithm’, IEEE Trans. Neural Netw., 1994, 5, (6), pp. 989993.
    8. 8)
      • 10. Battiato, S., Rundo, F., Stanco, F.: ‘ALZ: adaptive learning for zooming digital image’. IEEE Proc. of Int. Conf. Consumer and Electronics, Las Vegas, NV, USA, 2007, pp. 12.
    9. 9)
      • 7. Gonzalez, R.C., Woods, R.E.: ‘Digital image processing’ (Prentice-Hall, New York, NY, USA, 2018, 4th edn.).
    10. 10)
      • 9. Arena, P., Baglio, S., Fortuna, L., et al: ‘Dynamics of state controlled CNNs’. IEEE Proc. of Int. Symp. Circuits and Systems, ISCAS'96, Atlanta, GA, USA, 1996.
    11. 11)
      • 8. Chua, L.O., Yang, L.: ‘Cellular neural networks: theory’, IEEE Trans. Circuits Syst., 1988, 35, (10), pp. 12571272.
    12. 12)
      • 17. Battiato, S., Gallo, G., Stanco, F.: ‘A new edge-adaptive zooming algorithm for digital images’. Proc. Signal Processing and Communications SPC, 2000, pp. 144149.
    13. 13)
      • 1. Mendonça, T., Ferreira, P.M., Marques, J.S., et al: ‘PH2 – a dermoscopic image database for research and benchmarking’. 35th Int. Conf. the IEEE Engineering in Medicine and Biology Society, 3–7 July 2013, Osaka, Japan.
    14. 14)
      • 5. Jamil, U., Khalid, S., Usman Akram, M.: ‘Dermoscopic feature analysis for melanoma recognition and prevention’. Sixth Int. Conf. Innovative Computing Technology (INTECH), 2016.
    15. 15)
      • 12. 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, 2016.
    16. 16)
      • 4. Majtner, T., Yildirim-Yayilgan, S., Hardeberg, J.Y.: ‘Combining deep learning and hand-crafted features for skin lesion classification’. Sixth Int. Conf. Image Processing Theory, Tools and Applications (IPTA), 2016.
    17. 17)
      • 3. Conoci, S., Rundo, F, Petralia, S., et al: ‘Advanced skin lesion discrimination pipeline for early melanoma cancer diagnosis towards PoC devices’. IEEE Proc. the Circuit Theory and Design European Conf. (ECCTD), 4–6 September 2017, Catania.
    18. 18)
      • 18. Bengio, Y.: ‘Learning deep architectures for AI’, Found. Trends Mach. Learn., 2009, 2, (1), pp. 1127.
    19. 19)
      • 16. Xie, F., Fan, H, Li, Y., et al: ‘Melanoma classification on dermoscopy images using a neural network ensemble model’, IEEE Trans. Med. Imaging, 2017, 36, (3), pp. 849858.
    20. 20)
      • 2. Binu Sathiya, S., Kumar, S.S., Prabin, A.: ‘A survey on recent computer-aided diagnosis of melanoma’. 2014 Int. Conf. Control, Instrumentation, Communication and Computational Technologies (ICCICCT).
    21. 21)
      • 11. Lee, H., Ekanadham, C., Ng, A.Y.: ‘Sparse deep belief net model for visual area V2’, Adv. Neural. Inf. Process. Syst., 2007, 7, pp. 873880.

Related content

This is a required field
Please enter a valid email address