Author : Prema Gupta 1
Date of Publication :7th October 2020
Abstract: Skin cancer is the most common type of cancer, which affects the life of millions of people every year. About three million people are diagnosed with the disease every year in the United States alone. The rate of survival decreases steeply as the disease progresses. However, detection of skin cancer in the early stages is a difficult and expensive process. In this study, we propose a methodology that detects and identifies skin lesions as benign or malignant based upon images taken from general cameras. The images are segmented, features extracted by applying the rule and a Neural Network is trained to classify the lesions to a high degree of accuracy. artificial neural networks (ANNs) were used as diagnosis method for Skin cancer detection from magnetic resonance image. The detection of the cancer is performed in two stages: Preprocessing and enhancement in the first stage and segmentation and classification in the second stage which using different stages of statistical method; feature extraction which one of texture analysis and the last used this feature as input parameters to the feed-forward back propagation Artificial neural networks which designed by the neural networks toolbox in implemented all the result.
Reference :
-
- Andre Esteva, Brett Krupel and Sebastian Thrun, Deep Networks for Early Stage Skin Disease and Skin Cancer Classification, Stanford University, 2015.
- S. Shweta Jain, "ANN Approach Based On Back Propagation Network and Probabilistic Neural Network to Classify Skin Cancer," International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 3, August 2013.
- P. R. Hill, H. Bhaskar, M. E. Al-Mualla and D. R. Bull, Improved Illumination Invariant Homomorphic Filtering using the Dual Tree Complex Wavelet Transform, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, 2016, pp. 1214-1218.
- Gonzalez, R. C. and R. E. Woods, Digital Image Processing, Pearson Education, 2002.
- Sonali Patil and V. R. Udupi, Using Histogram Specification in a Hybrid Preprocessing Technique for Segmentation of Malignant Skin Lesions from Dermoscopic Images, International Journal of Computer Science Engineering and Information Technology Research, Vol. 5, Issue 4, 7182, August 2015.
- Noboyuki Otsu, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions On Systems, Man, And Cybernetics, Vol. SMC-9, No. 1, January 1979.
- Chiranjeev Sagar and Lalit Mohan Saini, Color Channel Based Segmentation of Skin Lesion from Clinical Images for the Detection of Melanoma, 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, 2016, pp. 1-5.
- N. F. M. Azmi, H. M. Sarkan, Y. Yahya and S. Chuprat, ABCD Rules Segmentation on Malignant Tumor and Benign Skin Lesion Images, 2016 3rd International Conference on Computer and Information Sciences (ICCOINS), Kuala Lumpur, 2016, pp. 66-70.
- Karen Simonyan and Andrew Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv:1409.155v6, 10th April 2015.
- R. Moussa, F. Gerges, C. Salem, R. Akiki, O. Falou and D. Azar, Computer-aided detection of Melanoma using geometric features, 2016 3rd Middle East Conference on Biomedical Engineering (MECBME), Beirut, 2016, pp. 125-128.
- Masood A., Al-Jumaily A.A., Adnan T., Development of Automated Diagnostic System for Skin Cancer: Performance Analysis of Neural Network Learning Algorithms for Classification, Wermter S. et al. (eds) Artificial Neural Networks and Machine Learning - ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham.
- Miss Hetal J. Vala and Prof. Astha Baxi, A Review on Otsu Segmentation Algorithm, International Journal of Advanced Research in Computer Engineering and Technology, Vol. 2, Issue 2, February 2013.
- R. Kasmi and K. Mokrani, Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule, in IET Image Processing, vol. 10, no. 6, pp. 448-455, 6 2016.
- Grammatikopoulos, G. and Hatzigaidas, A. and Papastergiou, A. and Lazaridis, P. and Zaharis, Z. and Kampitaki, D. and Tryfon, G., Automated Malignant Melanoma Detection Using MATLAB, Proceedings of the 5th WSEAS International Conference on Data Networks, Communications and Computers, DNCOCO’06, Bucharest, Romania, 2006.
- Marquardt, D.W., An Algorithm For Least-Squares Estimation of Nonlinear Parameters, Journal of the Society for Industrial and Applied Mathematics, Vol. 11, No. 2, June 1963.
- Martin Fodslette Moller., A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning, Neural Networks, Vol. 6, pp. 525-533, 1993.
- Murat Kayri, Predictive Abilities of Bayesian Regularization and Levenberg-Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data, Mathematical and Computational Applications, 2016.
- D. Foresee and M. Hagan, Gauss-Newton Approximation to Bayesian Learning, Neural Networks, Vol. 3, pp. 1930-1935, 1997.
- Girish B. Maru, Khushboo Gandhi, Asha Ramchandani, Gaurav Kumar,”The Role of Inflammation in Skin Cancer”,Year: May 2014, volume 816, DOI: 10.1007/978-3-0348-0837-817, Publisher Springer, Pages: 437-469.
- Azadeh Noori Hoshyar, Adel Al-Jumailya , Afsaneh Noori Hoshyar, ”The Beneficial Techniques in Preprocessing Step of Skin Cancer Detection System Comparing”, Procedia Computer Science 42, Year:2014 , Pages:25 – 31.
- Ivan S. Maksymov, Zhaoyang Zhang, Crosby Chang; Mikhail Kostylev, “Strong Eddy-Current Shielding of Ferromagnetic Resonance Response in SubSkin-Depth-Thick Conducting Magnetic Multilayers”, IEEE Magnetics Letters Year: 2014, Volume: 5, Number: 3500104, DOI: 10.1109/LMAG.2014.2379721.
- Harvey Lui, Jianhua Zhao, David McLean and Haisha n Zeng,”Real-time Raman Spectroscopy for In Vivo Skin Cancer Diagnosis”,Springer, Published May 2012, DOI: 10.1158/0008-5472.CAN-11-4061.
- Lu Wang, Anyu Li, Xin Tian, “Detection of Fruit Skin Defects Using Machine Vision System” 2013 Sixth International Conference on Business Intelligence and Financial Engineering, Year: 2013 , DOI: 10.1109/BIFE, Pages: 44 – 48.
- MA Calin, SV Parasca, R Savastru, “Optical techniques for the noninvasive diagnosis of skin cancer”, Springer-Verlag Berlin Heidelberg 2013, DOI 10.1007/s00432-013-1423-3, Pages:1083–1104
- Guan-Chun Luh ,“A multi-objective particle swarm optimization based threshold approach for skin color detection “ International Conference on Machine Learning and Cybernetics, Year: 2013, Volume: 03, DOI: 10.1109/ICMLC.2013.6890759, Pages: 1114 - 1119.