Search for Resolution Invariant Wavelet Features of Melanoma Learned by a Limited ANN Classifier

Grzegorz Surówka

This article addresses the Computer Aided Diagnosis (CAD) of melanoma pigmented skin cancer. We present back-propagated Artificial Neural Network (ANN) classifiers discriminating dermoscopic skin lesion images into two classes: malignant melanoma and dysplastic nevus. Features used for our classification experiments are derived from wavelet decomposition coefficients of the image. Our research objective is i) to select the most efficient topology of the hidden layers and the network learning algorithm for full-size and downgraded image resolutions and, ii) to search for resolution-invariant topologies and learning methods. The analyzed classifiers should be fit to work on ARM-based hand-held devices, hence we take into account only limited learning setups.
Słowa kluczowe: melanoma, CAD, wavelets, ANN

[1] Korotkov K., Garcia R., Computerized analysis of pigmented skin lesions: A review. Artificial Intelligence in Medicine, 2012, 56(2).

[2] Masood A., Ali Al-Jumaily A., Computer aided diagnostic support system for skin cancer: A review of techniques and algorithms. International Journal of Biomedical Imaging, 2013, 2013(7), pp. 323268.

[3] Oliveira R.B., Papa J.P., Pereira A.S., Tavares J.M.R., Computational methods for pigmented skin lesion classification in images: Review and future trends. Neural Computing and Applications, 2016.

[4] Skvara H., Teban L., Fiebiger M., Binder M., Kittler H., Limitations of dermoscopy in the recognition of melanoma. Arch. Dermatol., 2005, 141, pp. 155–160.

[5] Stolz W., Semmelmayer U., Johow K., Burgdorf W.H., Principles of dermatoscopy of pigmented skin lesions. Seminars in Cutaneous Medicine and Surgery, 2003, 22(1), pp. 9–20.

[6] Wang S.Q., Hashemi P., Noninvasive imaging technologies in the diagnosis of melanoma. Seminars in Cutaneous Medicine and Surgery, 2010, 29(3), pp. 174–184.

[7] Talbot H., Bischof L., An overview of the polartechnics solarscan melanoma diagnosis algorithms, 2003, pp. 33–38.

[8] Boone M., Suppa M., Dhaenens F., Miyamoto M., Marneffe A., Jemec G., Del Marmol V., Nebosis R., In vivo assessment of optical properties of melanocytic skin lesions and differentiation of melanoma from non-malignant lesions by high-definition optical coherence tomography. Arch. Dermatol. Res., 2016, 308(1), pp. 7–20.

[9] Johr R.H., Dermatoscopy: Alternative melanocytic algorithms - the abcd rule of dermatoscopy, menzies scoring method, and 7-point checklist. Clinics in Dermatology, 2002, 20, pp. 240–247.

[10] Kittler H., Pehamberger H., Wolff K., Binder M., Follow-up of melanocytic skin lesions with digital epiluminescence microscopy: Patterns of modifications observed in early melanoma, atypical nevi, and common nevi. J. Am. Acad. Dermatol., 2000, 43(3), pp. 467–476.

[11] Goodson A.G., Grossman D., Strategies for early melanoma detection: Approaches to the patient with nevi. J. Am. Acad. Dermatol., 2009, 60(5), pp. 719–735.

[12] Chang T., Kuo C.C., Texture analysis and classification with tree-structured wavelet transform. IEEE Transactions on Image Processing, 1993, 2(4), pp.  429–44.

[13] Walvick R.P., Patel K., Patwardhan S.V., Dhawan A.P., Classification of melanoma using wavelet-transform-based optimal feature set. In: Medical Imaging 2004, International Society for Optics and Photonics, 2004, pp. 944–951.

[14] Ma L., Staunton R.C., Analysis of the contour structural irregularity of skin lesions using wavelet decomposition. Pattern Recognition, 2013, 46(1), pp. 98–106.

[15] Massone C., Hofmann-Wellenhof R., Ahlgrimm-Siess V., Gabler G., Ebner C., Soyer H.P., Melanoma screening with cellular phones. PLoS ONE, 2007, 2(5), pp. e483.

[16] MacKinnon N., Vasefi F., Booth N., Farkas D.L., Melanoma detection using smartphone and multimode hyperspectral imaging. SPIE BiOS, 2016, 9711, pp. 971117–1.

[17] Sur´owka G., Ogorza lek M., On optimal wavelet bases for classification of melanoma images through ensemble learning. Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science,, 2016.

[18] Patwardhan S.V., Dhawan A.P., Relue P.A., Classification of melanoma using tree structured wavelet transforms. Computer Methods and Programs in Biomedicine, 2003, 72, pp. 223–239.

[19] Patwardhan S.V., Dai S., Dhawan A.P., Multi-spectral image analysis and classification of melanoma using fuzzy membership based partitions. Computerized Medical Imaging and Graphics, 2005, 29, pp. 287–296.

[20] Suro´wka G., Merkwirth C., Z˙ abin´ska-P lazak E., Graca A., Wavelet based classification of skin lesion images. Bio Alg. Med Syst., 2006, 2(4).

[21] Sur´owka G., Grzesiak-Kopec K., Different learning paradigms for the classification of melanoid skin lesions using wavelets. Proc. EMBC07 Lyon, 2007.

[22] Sur´owka G., Supervised learning of melanocytic skin lesion images. Proc. HSI Krak´ow, 2008.

[23] Indira D., Jyotsna Suprya P., Detection & analysis of skin cancer using wavelet techniques. International Journal of Computer Science and Information Technologies, 2011, 2(5), pp. 1927–1932.

[24] Fassihi N., Shanbehzadeh J., Sarrafzadeh H., Ghasemi E., Melanoma diagnosis by the use of wavelet analysis based on morphological operators. Proc. Int. Multiconf. Eng. Comp. Sci. I Hong-Kong, 2011.

[25] Castillejos H., Ponomaryov V., Nino-de Rivera L., Golikov V., Wavelet transform fuzzy algorithms for dermoscopic image segmentation. Computational and Mathematical Methods in Medicine, 2012, 578721.

[26] Ramteke N.S., Jain S.V., Analysis of skin cancer using fuzzy and wavelet technique - review & proposed new algorithm. International Journal of Engineering Trends and Technology, 2013, 4(6)

[27] Sugin S., Jegadeesh A., Segmentation of skin images using fixed grid wavelet networks. International Journal of Engineering Research & Technology, 2014, 3(4).

[28] Rajarathinam A., Arivazhagan A., Timely efficient automated system by segmentation using wavelet transform. International Journal of Science, Engineering and Technology Research, 2015, 4(8).

[29] Sikorski J., Identification of malignant melanoma by wavelet analysis. Proceedings of Student/Faculty Research Day, CSIS, Pace University, 2004.

[30] Aswin R., Jaleel J.A., Salim S., Implementation of ann classifier using matlab for skin cancer detection. ICMiC13, 2013, pp. 87–94.

[31] Mahmoud M.K.A., Al-Jumaily A., Takruri M., The automatic identification od melanoma by wavelet and curvelet analysis: Study based on neural network classification. 11th International Conference on Hybrid Intelligent Systems, 2011, pp. 680–685.

[32] Clawson K.M., Morrow P., Scotney B., McKenna J., Dolan O., Analysis of pigmented skin lesion border irregularity using the harmonic wavelet transform. Machine Vision and Image Processing Conf., 2009.

[33] Ercal F., Chawla A., Stoecker W.V., Lee H.C., Moss R.H., Neural network diagnosis of malignant melanoma from color images. IEEE Trans. Biomed. Eng., 1994, 41(9).

[34] Dreiseitl S., Ohno-Machado L., Kittler H., Vinterbo S., Billhardt H., Binder M., A comparison of machine learning methods for the diagnosis of pigmented skin lesions. Journal of Biomedical Informatics, 2001, 34, pp. 28–36.

[35] Rubegni P., Burroni M., Perotti R., Fimiani M., Andreassi L., Cevenini G., Dell’Eva G., Barbini P., Digital dermoscopy analysis and artificial neural network for the differentiation of clinically atypical pigmented skin lesions: A retrospective study. J. Invest. Dermatol., 2002, 119, pp. 471–474.

[36] Hoffmann K., Gambichler T., Rick A., Kreutz M., Anschuetz M., Gr¨unendick T., Orlikov A., Gehlen S., Perotti R., Andreassi L., et al., Diagnostic and neural analysis of skin cancer (danaos). A multicentre study for collection and computeraided analysis of data from pigmented skin lesions using digital dermoscopy. itish Journal of Dermatology, 2003, 149, pp. 801–809.

[37] Rajab M., Woolfson M., Morgan S., Application of region-based segmentation and neural network edge detection to skin lesions. Computerized Medical Imaging and Graphics, 2004, 28, pp. 61–68.

[38] Maglogiannis I., Pavlopoulos S., Koutsouris D., An integrated computer supported acquisition, handling, and characterization system for pigmented skin lesions in dermatological images. IEEE Trans. Inf. Techn. Biomed., 2005, 9(1).

[39] Zagrouba E., Barhoumi W., An accelerated system for melanoma diagnosis based on subset feature selection. Journal of Computing and Information Technology, 2005, 13(1), pp. 69–82.

[40] Iyatomi H., Oka H., Celebi M.E., Hashimoto M., Hagiwara M., Tanaka M., Ogawa K., An improved internet-based melanoma screening system with  dermatologist-like tumor area extraction algorithm. Computerized Medical Imaging and Graphics, 2008, 32(7), pp. 566–579.

[41] Schaefer G., Rajab M.I. Celebi M.E., Iyatomi,H., Skin lesion segmentation using cooperative neural network edge detection and colour normalization. Inf. Techn. and Applic. Biomed., 2009.

[42] Lau H.T., Al-Jumaily A., Automatically early detection of skin cancer: Study based on neural network classification. IEEE International Conference of Soft Computing and Pattern Recognition, 2009, pp. 375–380.

[43] Vennila G.S., Suresh L.P., Shunmuganathan K., Dermoscopic image segmentation and classification using machine learning algorithms. American Journal of Applied Sciences, 2012, 8(11).

[44] Mhaske H., Phalke D., Melanoma skin cancer detection and classification based on supervised and unsupervised learning. International conference on Circuits Controls and Communications, 2013, pp. 1–5.

[45] Jaleel J.A., Salim S., Aswin R., Computer aided detection of skin cancer. International Conference on Circuits, Power and Computing Technologies, 2013.

[46] Elgamal M., Automatic skin cancer images classification. International Journal of Advanced Computer Science and Applications, 2013, 4(3).

[47] Silva C.S., Marcal A.R., Colour-based dermoscopy classification of cutaneous lesions: An alternative approach. DOI: 10.1080/21681163.2013.803683, 2013.

[48] Achakanalli S., Sadashivappa G., Skin cancer detection and diagnosis using image processing and implementation using neural networks and abcd parameters, 2014.

[49] Alasadi A.H., ALsafy B.M., Early detection and classification of melanoma skin cancer. Int. J. Information Technology and Computer Science, 2015, 12, pp. 67–74.

[50] Torre E.L., Caputo B., Tommasi T., Learning methods for melanoma recognition. International Journal of Imaging Systems and Technology, 2010, 20(4), pp. 316–322.

[51] Ruiz D., Berenguer V., Soriano A., S´aNchez B., A decision support system for the diagnosis of melanoma: A comparative approach. Expert Systems with Applications, 2011, 38, pp. 15217–15223.

[52] Maglogiannis I., Kosmopoulos D.I., Computational vision systems for the detection of malignant melanoma. Oncology Reports, 2006, 15(Spec no. 4), pp. 1027–32.

[53] Rajpara S., Botello A., Townend J., Ormerod A., Systematic review of dermoscopy and digital dermoscopy/artificial intelligence for the diagnosis of melanoma. British Journal of Dermatology, 2009, 161(3), pp. 591–604.

[54] Sathiya S.B., Kumar S., Prabin A., A survey on recent computer-aided diagnosis of melanoma. International Conference on Control Instrumentation Communication and Computational Technologies, 2014, pp. 1387–1392.

[55] Abedini M., Chen Q., Codella N.C., Garnavi R., Sun X., Accurate and scalable system for automatic detection of malignant melanoma. In book: Dermoscopy Image Analysis, 2015, pp. 293–343.

[56] Chawla N.V., Bowyer K.W., Hall L.O., Kegelmeyer W.P., Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 2002, 16, pp. 321357.

[57] Stefanowski J., Wilk S., Selective pre-processing of imbalanced data for improving classification performance. Data Warehousing and Knowledge Discovery, 2008, pp. 283–292.

[58] Rastgoo M., Lemaitre G., Massich J., Morel O., Marzani F., Garcia R., Meriaudeau, F., Tackling the problem of data imbalancing for melanoma classification. BIOSTEC - 3rd International Conference on BIOIMAGING, 2016.

[59] Mallat S.G., A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on pattern analysis and machine intelligence, 1989, 11(7).

[60] Daubechies I., Ten lectures on wavelets. CBMS SIAM, 1994, 61.

[61] Tang J., Alelyani S., Liu H., Feature selection for classification: A review. CRC Press, 2014, 37.

[62] Maglogiannis I., Doukas C.N., Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans. Inf. Techn. Biomed., 2009, 13(5), pp. 721–733.

[63] Michie D., Spiegelhalter D.J., Taylor C.C., Machine Learning, Neural and Statistical Classification. Prentice Hall, 1994.

[64] Haykin S., Neural Networks: A Comprehensive Foundation. 2 edn. Prentice Hall, 2004 ISBN 0-13-273350-1.

[65] Demuth H.B., Beale M.H., De Jess O., Hagan M.T., Neural Network Design. 2 edn., 2004 ISBN-10: 0-9717321-1-6, ISBN-13: 978-0-9717321-1-7.

[66] Battiti R., First- and second-order methods for learning: Between steepest descent and newton’s method. Neural Computation, 1992, 4(2).

[67] Hajian-Tilaki K., Receiver operating characteristic (roc) curve analysis for medical diagnostic test evaluation. Caspian J. Intern. Med., 2013, 4(2), pp. 627–635.

Czasopismo ukazuje się w sposób ciągły on-line.
Pierwotną formą czasopisma jest wersja elektroniczna.

Wersja papierowa czasopisma dostępna na