Skin Cancer Diagnosis using Cascaded Correlation Neural Network
Keywords:
Malignant Melanoma, Histogram equalization, feature extraction, colour space, cascaded correlation neural network, Receiver Operating CharacteristicAbstract
In recent days, Melanoma is found to be the most unpredictable and fatal form of skin disease. But it is curable if detected at the rudimentary stage. In this paper, Cascaded Correlation Neural Network, a new method of automatic classification of the skin images is presented. CCNN is self-organizing networks which by itself trains and add on new hidden layers consecutively till the error is minimized. By adopting this particular feature, an accurate and efficient image processing technique is implemented in this paper for cancer detection. As a preprocessing step, noise is filtered, and contrast enhancement is done using histogram equalization method. The color attributes are taken from RGB and opponent color space in the skin lesion and are provided as input to the CCNN. The proposed approach is tested on the ISIC database of melanoma images. Receiver Operating Characteristic curve is used to detect the performance of the suggested system. In the results obtained with 91.1 % accuracy, the sensitivity is 91.7% and the specificity is 89.2%. The result shows the potential of the proposed CCNN network.
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A. F. Jerant, J. T. Johnson, C. D. Sheridan, and T. J. Caffey, “Early detection and treatment of skin cancer,” American Family Physician, vol.62, no. 2, pp. 381-382, 2000.
B.K. Brar, N.Sethi and EraK “An Epidemiological review of Skin Cancers in Malwa belt of Punjab India: A 3-year Clinicopathological Study”, Scholar Journal of Applied Medical Sciences, Vol. 3, pp.3405- 3408, 2015.
Pratik D, Sankirtan B, Chaitanya J,and Dr. Sonali P, “Skin Cancer Detection and Classification”, International Conference on Electrical Engineering and Informatics (ICEEI), pp. 1-6, 2017.
Ammara M and Adel Ali A, “Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms”, International Journal of Biomedical Imaging, vol. 2013, 2013.
D.Gautam, and M.Ahmed,” Melanoma Detection and Classification Using SVM Based Decision Support System”, 2015 Annual IEEE India Conference (INDICON), pp. 1-6, 2015.
S.Mustafa, A.B.Dauda, M.Daud, “Image Processing and SVM Classification for Melanoma Detection”, 2017 International Conference on Computing Networking and Informatics (ICCNI), pp. 1-5, 2017.
S. M. Kumar, J. R.Kumar,and K. Gopalakrishnan,” Skin Cancer Diagnostic using Machine Learning Techniques - Shearlet Transform and Naïve Bayes Classifier”, International Journal of Engineering and Advanced Technology, vol. 9, no. 2, 2019.
N.Fassihi, J.Shanbehzadeh, A.Sarafzadeh, and E.Ghasemi, “Melanoma Diagnosis by the Use of Wavelet Analysis based on Morphological Operators”, Proceedings of the International Multiconference of Engineers and Computer scientists , Vol I, 2011.
R. Zhang, "Melanoma Detection Using Convolutional Neural Network," 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 75-78, 2021.
R.Garnavi, M.Aldeenand J. Bailey, “Computer-aided Diagnosis of Melanoma Using Border and Wavelet-based Texture Analysis”: IEEE Transactions on Information Technology in Biomedicine, vol. 6, no. 6 ,2012.
A.Hekler, J. S. Utikal, et al. “Superior skin cancer classification by the combination of human and artificial intelligence” European Journal of Cancer, Vol. 120, pp. 114-121, 2019.
J.Premaladha and K.S Ravichandran, “Asymmetry analysis of Malignant Melanoma using image processing, A Survey”, Journal on Artificial Intelligence, vol. 2, pp.45-53, 2014.
N.Nidaa , A. Irtazab et al., “Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering”, International Journal of Medical Informatics, vol. 124, pp. 37-48, 2019.
L.Yu, H.Chen, Qi Dou, J.Qin, and Pheng-Ann Heng, “Automated Melanoma Recognition in Dermoscopy images via Very Deep Residual Networks” IEEE Transactions on Medical Imaging, vol. 36, no. 4, pp. 994-1004, 2017.
E.Pichon, M.Niethammer, and G.Sapiro, “Color Histogram Equalization Through Mesh Deformation, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), 2003, pp. II-117, 2003.
S.Sural, G.Qian and S.Pramanik, “Segmentation And Histogram Generation Using The HSV Color Space for Image Retrieval”, Proceedings. International Conference on Image Processing, 2002, pp. II-II, 2002.
Sapana S. Bagade, and Vijaya K. Shandilya,” Use of Histogram Equalization in Image Processing for Image Enhancement”, International Journal of Software Engineering Research & Practices, vol 1, no. 2, 2011.
A. R. Weeks, L. J. Sartor, and H. R. Myler, “Histogram specification of 24-bit color images in the color difference (C-Y) color space”, Proc. SPIE 3646, Nonlinear Image Processing X, SPIE Vol. 3646,1991
Tetko, I.V., Kovalishyn, V.V., Luik, A.I., Kasheva, T.N., Villa, A.E.P., Livingstone, D.J.. “Variable Selection in the Cascade-Correlation Learning Architecture”. In: Gundertofte, K., Jørgensen, F.S. (eds) Molecular Modeling and Prediction of Bioactivity. Springer, Boston, MA, 2000.
Ginu George, Rinoy M. O, et al., “Survey on Various Median Filtering Techniques For Removal of Impulse Noise From Digital Image”, Conference on Emerging Devices and Smart Systems, 2018.
K. Nallaperumal et al., "An analysis of suitable color space for visually plausible shadow-free scene reconstruction from single image," 2013 IEEE International Conference on Computational Intelligence and Computing Research, 2013, pp. 1-5.
M. Li and X. Jiang, "An Improved Algorithm Based on Color Feature Extraction for Image Retrieval," 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2016, pp. 281-285.
Andrew E Bradley, “The use of the area under the ROC curve in the evaluation of machine learning algorithms”, Pattern Recognition, vol. 30, pp. 1145-1159, 1997.
Youden WJ,” An index for rating diagnostic tests”, Cancer, 1950.
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