Analysing the Performance of Classifiers for the Detection of Skin Cancer with Dermoscopic Images
Kavimathi.P, Sri Venkateswara College Of Engineering; Sivagnanasubramanian.S.P ,
Skin cancer, Feature extraction, Adaptive Neurofuzzy inference system, Thresholding
Skin cancer is one of the major causes of deaths in recent days. Early detection of skin cancer reduces death at higher rate. Ceroscopy is one of the major modalities used in diagnosis of skin lesions. Skin lesions are of different types. Among them the most common types of skin lesion found in human are melanoma, basal cell carcinoma (BCC) and squamous cell carcinoma (SCC).The accurate diagnosis information cannot be obtained by human interpretation. In order to overcome the error due to human interpretation an efficient computerized image analysis system has been developed. The proposed image analysis system consists of preprocessing, lesion segmentation, feature extraction and classification. In classification, different types of classifiers such as support vector machine (SVM), probabilistic neural network (PNN) and adaptive neurofuzzy inference system (ANFIS) are applied to classify the skin cancer types and their performance is compared using the evaluated parameters.
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[1] S.G.Mallat,“A Theory for Multiresolution, (1989) Signal Decomposition: The Wavelet Representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 7, no. 11, pp. 674–93.
[2] P.Bilek,A. B. Cognetta,M. Landthaler, T. Merckle,F. Nachbar,G. Plewig, W. Stolz and T. Vogt(1994)“The ABCD ruleof dermatoscopy:High prospective value in the diagnosis of doubtful melanocytic skin lesions,” J. Amer. Acad. Dermatol., vol. 30, pp.551–559.
[3] R. Mahmud,A. Ramli, M.Al-Qdaha, (2005) “A system of Micro-Calcifications Detection and Evaluation of the Radiologist: Comparative Study of the three main races in Malaysia”, Elsevier Journal of Computers in Biology and Medicine vol. 35, no. 10.
[4] F.Godtliebsen,H. M. Kirchesch,K. Møllersen, T. G. Schopf, and ,(2010)``Unsupervised segmentation for digital dermoscopic images,'' Skin Res.Technol., vol. 16, no. 4, pp. 401_407.
[5] M. S. Atkins, T. K. Lee, N. H. Nguyen,(2010) and ``Segmentation of light and dark hair in dermoscopic images: A hybrid approach using a universal kernel,'' Proc. SPIE, vol. 7623, p. 76234N.
[6] Q. Abbas, I. Fondon, and M. Rashid,(2011) ``Unsupervised skin lesions border detection via two-dimensional image analysis,''Comput. Methods Programs Biomed., vol. 104, no. 3, pp. e1_e15.
[7] Dimitri Dubovitski, Jonathan Blackledge,(2011) “Mole test: A Web-based Skin Cancer Screening System”, Intensive 2011: The Third International Conference on Resource Intensive Applications and Services, vol: 978-1-61208-006-2, pp. 22 – 29.
[8] M.d.KhaladAbuMahmoud,Wighton.P,(2011)“The Automatic Identification of Melanoma by Wavelet and Curve let Analysis: Study Based on Neural Network Classification”, 11th IEEE International Conference on Hybrid Intelligent Systems (HIS), pp: 680- 685.
[9] A.Karargyris,O.Karargyris,andDERMA/Care,(2012)Advanced image-processing mobile application for monitoring skincancer,'' in Proc. IEEE 24th Int. Conf. Tools Artif. Intell. (ICTAI), pp. 1_7.
[10] Nilkamal S. Ramteke, Shweta V. Jain,(2013) “Analysis of Skin Cancer Using Fuzzy and Wavelet Technique –Review & Proposed New Algorithm” International Journal of Engineering Trends and Technology(IJETT) ,Volume 4, Issue 6
[11] Sookpotharom Supot,(2014) “Skin Lesion Detection of Dermoscopy Images Using Estimate Localization Technique”, pg. no. 833-836.
[12] http://www.healthline.com/health/skin-lesions
[13] http://en.wikipedia.org/wiki/Skin_cancer
Paper ID: GRDCF002059
Published in: Conference : International Conference on Innovations in Engineering and Technology (ICIET - 2016)
Page(s): 437 - 442
Published in: Conference : International Conference on Innovations in Engineering and Technology (ICIET - 2016)
Page(s): 437 - 442