Pigmented Skin Lesions Classification Using Data Driven Subsets of Image Features

In this paper we present an architecture for identification of pigmented skin lesions from dermatoscopic images. The architecture used a large number of image features and was evaluated with several classification algorithms on different feature subsets as extracted from feature ranking. The best performing classification algorithm was the support vector machines using polynomial kernel function with classification accuracy equal to 74.69% and the most precisely classified skin lesion type between seven different skin pathologies was nevus with accuracy equal to 94.38%.

[1]  P. Royal.  Diagnostic accuracy in skin cancer clinics:The Australian experience , 2006 .

[2]  T Lee,et al.  Dullrazor®: A software approach to hair removal from images , 1997, Comput. Biol. Medicine.

[3]  Noel C. F. Codella,et al.  Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC) , 2019, ArXiv.

[4]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[5]  I. Weatherall,et al.  Skin color measurements in terms of CIELAB color space values. , 1992, The Journal of investigative dermatology.

[6]  A. Jerant,et al.  Early detection and treatment of skin cancer. , 2000, American family physician.

[7]  Gerard de Haan,et al.  Automatic imaging sysem with decision support for inspection of pigmented skin lesions and melanoma diagnosis. , 2009 .

[8]  Joachim M. Buhmann,et al.  Nonparametric Bayesian Image Segmentation , 2008, International Journal of Computer Vision.

[9]  Xiaoxiao Li,et al.  Skin Lesion Classification Via Combining Deep Learning Features and Clinical Criteria Representations , 2018 .

[10]  Achmad Benny Mutiara,et al.  Classification of Melanoma Skin Cancer using Convolutional Neural Network , 2019, International Journal of Advanced Computer Science and Applications.

[11]  W. Stolz,et al.  The ABCD rule of dermatoscopy. High prospective value in the diagnosis of doubtful melanocytic skin lesions. , 1994, Journal of the American Academy of Dermatology.

[12]  Mai S. Mabrouk,et al.  Automated Imaging System for Pigmented Skin Lesion Diagnosis , 2016 .

[13]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[14]  D. S. Guru,et al.  Segmentation and Classification of Skin Lesions for Disease Diagnosis , 2016, ArXiv.

[15]  S. Sathiya Keerthi,et al.  Improvements to Platt's SMO Algorithm for SVM Classifier Design , 2001, Neural Computation.

[16]  W. Chang,et al.  Computer-Aided Diagnosis of Skin Lesions Using Conventional Digital Photography: A Reliability and Feasibility Study , 2013, PloS one.

[17]  Josiane Zerubia,et al.  Classification of skin hyper-pigmentation lesions with multi-spectral images , 2012 .

[18]  David A Clausi.  An analysis of co-occurrence texture statistics as a function of grey level quantization , 2002 .

[19]  Pietro Rubegni,et al.  Automated diagnosis of pigmented skin lesions , 2002, International journal of cancer.

[20]  Marko Robnik-Sikonja,et al.  An adaptation of Relief for attribute estimation in regression , 1997, ICML.

[21]  J. Ross Quinlan,et al.  Simplifying Decision Trees , 1987, Int. J. Man Mach. Stud..

[22]  Peter Trovitch,et al.  Early detection and treatment of skin cancer , 2002 .

[23]  Manohar Kuse,et al.  Local isotropic phase symmetry measure for detection of beta cells and lymphocytes , 2011, Journal of pathology informatics.

[24]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[25]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[26]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[27]  Leen-Kiat Soh,et al.  Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices , 1999, IEEE Trans. Geosci. Remote. Sens..

[28]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[29]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[30]  Ghassan Hamarneh,et al.  Deep features to classify skin lesions , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[31]  Yasser Baleghi,et al.  Skin lesion images classification using new color pigmented boundary descriptors , 2017, 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA).

[32]  Germán Capdehourat,et al.  Pigmented Skin Lesions Classification Using Dermatoscopic Images , 2009, CIARP.

[33]  Harald Kittler,et al.  Descriptor : The HAM 10000 dataset , a large collection of multi-source dermatoscopic images of common pigmented skin lesions , 2018 .

[34]  David W. Aha,et al.  Instance-Based Learning Algorithms , 1991, Machine Learning.

[35]  M. Binder,et al.  Epiluminescence microscopy. A useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists. , 1995, Archives of dermatology.

[36]  Julie Ann A. Salido,et al.  HAIR ARTIFACT REMOVAL AND SKIN LESION SEGMENTATION OF DERMOSCOPY IMAGES , 2018 .