Melanoma recognition using extended set of descriptors and classifiers

The paper presents a novel method of melanoma recognition on the basis of dermoscopic images. We use color images of skin lesions, advanced image processing, and different classifiers to distinguish melanoma from the other non-melanoma lesions. Different families of descriptors are used for generation of the image diagnostic features for final pattern recognition. To increase the efficiency of the system, we apply different selection procedures to find the best set of features and different solutions of classifier. The numerical results concerning the accuracy of the proposed recognition system have confirmed good accuracy of the proposed method and high sensitivity in melanoma recognition.

[1]  Yuan-Ting Zhang,et al.  Guest Editorial Introduction to the Special Section: 4G Health - The Long-Term Evolution of m-Health , 2012, IEEE Trans. Inf. Technol. Biomed..

[2]  Ahmad R. Sharafat,et al.  E-shaver: An improved DullRazor® for digitally removing dark and light-colored hairs in dermoscopic images , 2011, Comput. Biol. Medicine.

[3]  Gregory W. Corder,et al.  Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach , 2009 .

[4]  Junji Maeda,et al.  Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images , 2009, IEEE Journal of Selected Topics in Signal Processing.

[5]  Stuart M. Goldsmith,et al.  A unifying approach to the clinical diagnosis of melanoma including “D” for “Dark” in the ABCDE criteria , 2014, Dermatology practical & conceptual.

[6]  Ryszard Tadeusiewicz,et al.  Neural networks: A comprehensive foundation: by Simon HAYKIN; Macmillan College Publishing, New York, USA; IEEE Press, New York, USA; IEEE Computer Society Press, Los Alamitos, CA, USA; 1994; 696 pp.; $69–95; ISBN: 0-02-352761-7 , 1995 .

[7]  Peter E. Hart,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[8]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[9]  Agma J. M. Traina,et al.  An Efficient Algorithm for Fractal Analysis of Textures , 2012, 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images.

[10]  Begoña García Zapirain,et al.  Melanomas non-invasive diagnosis application based on the ABCD rule and pattern recognition image processing algorithms , 2011, Comput. Biol. Medicine.

[11]  Rita Cucchiara,et al.  A new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions , 2003, IEEE Transactions on Medical Imaging.

[12]  Mark A. Hall,et al.  Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning , 1999, ICML.

[13]  John Collins,et al.  A cascade classifier for diagnosis of melanoma in clinical images , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  Ezzeddine Zagrouba,et al.  A PRELIMARY APPROACH FOR THE AUTOMATED RECOGNITION OF MALIGNANT MELANOMA , 2011 .

[15]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[16]  Harald Ganster,et al.  Automated Melanoma Recognition , 2001, IEEE Trans. Medical Imaging.

[17]  Jean-Baptiste Poline,et al.  Unsupervised robust nonparametric estimation of the hemodynamic response function for any fMRI experiment , 2003, IEEE Transactions on Medical Imaging.

[18]  S. Redner,et al.  Introduction To Percolation Theory , 2018 .

[19]  Pedro M. Ferreira,et al.  PH2 - A dermoscopic image database for research and benchmarking , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[20]  Aglaia G. Manousaki,et al.  Use of color texture in determining the nature of melanocytic skin lesions - a qualitative and quantitative approach , 2006, Comput. Biol. Medicine.

[21]  Linda G. Shapiro,et al.  Image Segmentation Techniques , 1984, Other Conferences.

[22]  Bareqa Salah,et al.  Skin Cancer Recognition by Using a Neuro-Fuzzy System , 2011, Cancer informatics.

[23]  A. Robert Calderbank,et al.  Melanoma classification from Hidden Markov Tree features , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[24]  Pang-Ning Tan,et al.  Introduction To Data Mining”, Person Education, 2007 , 2015 .

[25]  Jorge S. Marques,et al.  Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features , 2014, IEEE Systems Journal.

[26]  Stephen W Dusza,et al.  ‘Do UC the melanoma?’ Recognising the importance of different lesions displaying unevenness or having a history of change for early melanoma detection , 2014, The Australasian journal of dermatology.

[27]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[28]  Huan Liu,et al.  Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution , 2003, ICML.

[29]  Randy H. Moss,et al.  A methodological approach to the classification of dermoscopy images , 2007, Comput. Medical Imaging Graph..

[30]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[31]  James Bailey,et al.  Computer-Aided Diagnosis of Melanoma Using Border- and Wavelet-Based Texture Analysis , 2012, IEEE Transactions on Information Technology in Biomedicine.

[32]  Stanislaw Osowski,et al.  Texture characterization based on the Kolmogorov-Smirnov distance , 2015, Expert Syst. Appl..