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Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

Skin Disease Classification using Neural Network

Author(s): Usama Ijaz Bajwa*, Sardar Alam, Nuhman ul Haq, Naeem Iqbal Ratyal and Muhammad Waqas Anwar

Volume 16, Issue 6, 2020

Page: [711 - 719] Pages: 9

DOI: 10.2174/1573405615666190422152926

Price: $65

Abstract

Background: In this study, a novel and fully automatic skin disease classification approach is proposed using statistical feature extraction and Artificial Neural Network (ANN) based classification using first and second order statistical moments, the entropy of different color channels and texture-based features.

Aims: The basic aim of our study is to develop an automated system for skin disease classification that can help a general physician to automatically detect the lesion and classify it to disease types.

Method: The performance of the proposed approach is corroborated by extensive experiments performed on a dataset of 588 images containing 6907 lesion regions.

Results: The results show that the proposed methodology can be effectively used to construct a skin disease classification system.

Conclusion: Our proposed method is designed for a specific skin tone. Future investigation is needed to analyze the impact of different skin tones on the performance of lesions detection and classification system.

Keywords: Skin disease, artificial neural network, classification, medical abnormalities, lesion, Ultraviolet (UV) rays.

Graphical Abstract
[1]
Hay RJ, Johns NE, Williams HC, et al. The global burden of skin disease in 2010: an analysis of the prevalence and impact of skin conditions. J Invest Dermatol 2014; 134(6): 1527-34.
[http://dx.doi.org/10.1038/jid.2013.446] [PMID: 24166134]
[2]
Ascierto PA, Palmieri G, Celentano E, et al. Sensitivity and specificity of epiluminescence microscopy: evaluation on a sample of 2731 excised cutaneous pigmented lesions. The Melanoma Cooperative Study. Br J Dermatol 2000; 142(5): 893-8.
[http://dx.doi.org/10.1046/j.1365-2133.2000.03468.x] [PMID: 10809845]
[3]
Celebi ME, Iyatomi H, Schaefer G, Stoecker WV. Lesion border detection in dermoscopy images. Comput Med Imaging Graph 2009; 33(2): 148-53.
[http://dx.doi.org/10.1016/j.compmedimag.2008.11.002] [PMID: 19121917]
[4]
Silveira M, Nascimento JC, Marques JS, et al. Comparison of segmentation methods for melanoma diagnosis in dermoscopy images. IEEE J Sel Top Signal Process 2009; 3(1): 35-45.
[http://dx.doi.org/10.1109/JSTSP.2008.2011119]
[5]
Celebi ME, Kingravi HA, Iyatomi H, et al. Border detection in dermoscopy images using statistical region merging. Skin Res Technol 2008; 14(3): 347-53.
[http://dx.doi.org/10.1111/j.1600-0846.2008.00301.x] [PMID: 19159382]
[6]
Acne - Symptoms and causes [Homepage on the Internet] [Cited 2018 Jan 02] Available from:. https: //www.mayoclinic.org/ diseases-conditions/acne/symptoms-causes/syc-20368047
[7]
Eczema [Homepage on the Internet] [Cited 2018 Feb 03] Available from:. https://nationaleczema.org/eczema/
[8]
Psoriasis Symptoms [Internet] [Cited 03-Feb-2018] Available from:. https://www.webmd.com/skin-problems-and-treatments/psoriasis/psoriasis-signs-symptoms
[9]
Scabies [Homepage on the Internet][Cited 2018 Feb 03] Available from:. https://www.aad.org/public/diseases/contagious-skin-diseases/scabies
[10]
Adams BB. Tinea corporis gladiatorum. J Am Acad Dermatol 2002; 47(2): 286-90.
[http://dx.doi.org/10.1067/mjd.2002.120603] [PMID: 12140477]
[11]
Vitiligo-symptoms and causes [Homepage on the Internet] [Cited 2018 Dec 14] Available from:. https://www.mayoclinic.org/diseases-conditions/vitiligo/symptoms-causes/syc-20355912
[12]
Gao J, Zhang J, Fleming MG, Pollak I, Cognetta AB. Segmentation of dermatoscopic images by stabilized inverse diffusion equations. In: Proceedings of the 1998 International Conference on Image Processing ICIP98 (Cat No98CB36269);. Chicago, IL, USA; IEEE; 1998; pp. 823-7.
[13]
Fleming MG, Steger C, Zhang J, et al. Techniques for a structural analysis of dermatoscopic imagery. Comput Med Imaging Graph 1998; 22(5): 375-89.
[http://dx.doi.org/10.1016/S0895-6111(98)00048-2] [PMID: 9890182]
[14]
Yamaguchi M, Mitsui M, Murakami Y, Fukuda H, Ohyama N, Kubota Y. Multispectral color imaging for dermatology: application in inflammatory and immunologic diseases. In: Proceedings of the 13th Color and Imaging Conference. Scottsdale, AZ, USA 2005; pp. 52-8.
[15]
Razazzadeh N, Khalili M. Automatic DWT2 thresholding based segmentation of the pigmented skin lesions in dermatoscopic images. Int J Eng Technol 2014; 3(4): 529-34.
[http://dx.doi.org/10.14419/ijet.v3i4.3536]
[16]
Arifin MS, Kibria MG, Firoze A, Amini MA, Yan H. Dermatological disease diagnosis using color-skin images. In: Proceedings of the International Conference on Machine Learning and Cybernetics. Xian, China. Piscataway, NJ: IEEE 2012; pp. 1675-80.
[17]
Lee T, Ng V, Gallagher R, Coldman A, McLean D. DullRazor: a software approach to hair removal from images. Comput Biol Med 1997; 27(6): 533-43.
[http://dx.doi.org/10.1016/S0010-4825(97)00020-6] [PMID: 9437554]
[18]
Kiani K, Sharafat AR. E-shaver: an improved DullRazor(®) for digitally removing dark and light-colored hairs in dermoscopic images. Comput Biol Med 2011; 41(3): 139-45.
[http://dx.doi.org/10.1016/j.compbiomed.2011.01.003] [PMID: 21316042]
[19]
Wighton P, Lee TK, Atkins MS. Dermascopic hair disocclusion using inpainting. Proceedings of the Medical Imaging 2008: Image Processing; San Diego, CA, USA 2008.
[http://dx.doi.org/10.1117/12.770776]
[20]
Abbas Q, Fondón I, Rashid M. Unsupervised skin lesions border detection via two-dimensional image analysis. Comput Methods Programs Biomed 2011; 104(3): e1-e15.
[http://dx.doi.org/10.1016/j.cmpb.2010.06.016] [PMID: 20663582]
[21]
Abbas Q, Garcia IF, Emre Celebi M, Ahmad W. A feature-preserving hair removal algorithm for dermoscopy images. Skin Res Technol 2013; 19(1): e27-36.
[http://dx.doi.org/10.1111/j.1600-0846.2011.00603.x] [PMID: 22211360]
[22]
Chiem A, Al-Jumaily A, Khushaba RN. A novel hybrid system for skin lesion detection. In: Proceedings of the 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information;. Melbourne, Australia; IEEE 2007; pp. 567-72.
[http://dx.doi.org/10.1109/ISSNIP.2007.4496905]
[23]
Ruiz D, Berenguer V, Soriano A, Sánchez B. A decision support system for the diagnosis of melanoma: A comparative approach. Expert Syst Appl 2011; 38(12): 15217-23.
[http://dx.doi.org/10.1016/j.eswa.2011.05.079]
[24]
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. J Biomed Inform 2001; 34(1): 28-36.
[http://dx.doi.org/10.1006/jbin.2001.1004] [PMID: 11376540]
[25]
Antkowiak M. Artificial neural networks vs support vector machines for skin diseases recognition. Umea University, Umea, Sweden 2006.
[26]
Kabari LG, Bakpo FS. Diagnosing skin diseases using an artificial neural network. In: 2nd International Conference on Adaptive Science & Technology (ICAST). Accra, Ghana 2009; pp. 187-91.
[27]
Oliveira RB, Filho ME, Ma Z, Papa JP, Pereira AS, Tavares JMRS. Computational methods for the image segmentation of pigmented skin lesions: A review. Comput Methods Programs Biomed 2016; 131: 127-41.
[http://dx.doi.org/10.1016/j.cmpb.2016.03.032] [PMID: 27265054]
[28]
Naji SA, Zainuddin R, Jalab HA. Skin segmentation based on multi pixel color clustering models. Digit Signal Process 2012; 22(6): 933-40.
[http://dx.doi.org/10.1016/j.dsp.2012.05.004]
[29]
Hojjatoleslami A, Avanaki MRN. OCT skin image enhancement through attenuation compensation. Appl Opt 2012; 51(21): 4927-35.
[http://dx.doi.org/10.1364/AO.51.004927] [PMID: 22858930]
[30]
Adabi S, Hosseinzadeh M, Noei S, et al. Universal in vivo textural model for human skin based on optical coherence tomograms. Sci Rep 2017; 7(1): 17912.
[http://dx.doi.org/10.1038/s41598-017-17398-8] [PMID: 29263332]
[31]
Quiñonero-Candela J, Sugiyama M, Schwaighofer A, Lawrence ND. Dataset shift in machine learning (neural information processing). Cambridge, Massachusetts, London, England: The MIT Press 2009.

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