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  A Comparative Study of Various Techniques for Skin Cancer Detection  
  Authors : Ramandeep Kaur; Gagandeep; Parveen Kumar; Geetanjali Babbar
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Early detection of melanoma skin cancer is critical for effective treatment. In recent times, it is well known that the most hazardous form of skin cancer is melanoma, as it is spread out very fatly in the entire body parts. Therefore, it needs to be treated at its earliest stage. The image processing plays an essential role in the detection and classification of diseases on the images obtained from the digital clinic. In this paper, the existing non invasive techniques are studied which provides an automated image analysis tools and statistics for accurate and rapid assessment of lesions in the images. The performance of several classifiers specifically for the diagnosis of skin lesions is compared and also discussed the corresponding findings. It is found that the artificial intelligence technique performed well when it is integrated with other optimization technique.


Published In : IJCSN Journal Volume 8, Issue 2

Date of Publication : April 2019

Pages : 187-191

Figures :05

Tables : 02


Ramandeep Kaur : is Currently pursuing M.TECH in computer science and engineering at Chandigarh Engineering College (CEC), Landran, Mohali, India. Her Research Interest includes medical image processing.

Dr. Gagandeep : is presently working as Professor in Department of Computer Science & Engineering, Chandigarh Engineering College (CEC), Landran, Mohali, India. He received M.E. degree from PEC University of Technology, Chandigarh, India, in 2005 and Ph.D. degree in Computer Engineering from Panjabi university, Patiala, India, in 2017. His current research interests include medical image processing, object detection, semantic retrieval etc.

Parveen Kumar : Chandigarh engineering college Landran,Punjab Techincal university, Jalandhar,India.

Geetanjali Babbar : Chandigarh engineering college Landran,Punjab Techincal university, Jalandhar,India.


Skin Cancer, Lesion, Melanoma, GA, ABCD, ANN, SVM

In this paper, the various non-invasive techniques for classification and detection of skin cancer have been studied. The detection process of melanoma is carried out in different stages such as pre-processing, segmentation, feature extraction and classification. The investigation focused on a number of strategies such as GA, SVM, ANN, and ABCD rules. From the analysis, it has been investigated that the ABCD approach with Backpropagation Neural network has performed well compared to other mentioned schemes with an accuracy rate of about 95 %. In future experiments, optimization algorithm can be used such as GA with multiclass classification scheme like ANN to enhance the performance of the skin cancer detection and classification system.


[1] S. Sigurdsson, P. A. Philipsen, L. K. Hansen, J. Larsen, M. Gniadecka,, & H. C. Wulf, "Detection of skin cancer by classification of Raman spectra", IEEE transactions on biomedical engineering, Vol.51, No.10, 2004, pp.1784-1793. [2] A. Masood, & A. Ali Al-Jumaily, "Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms", International journal of biomedical imaging, 2013. [3] M. A. Sheha, M. S. Mabrouk, & A. Sharawy, "Automatic detection of melanoma skin cancer Using texture analysis", International Journal of Computer Applications, Vol.42, No.20, 2012, pp. 922-26. [4] S. Jain, & N. Pise, "Computer aided melanoma skin cancer detection using image processing", Procedia Computer Science, Vol. 48, 2015, pp.735-740. [5] D. S. Rigel, R. J. Friedman, A. W. Kopf, R. Weltman,P. G. Prioleau, "Importance of complete Cutaneous examination for the detection of Malignant melanoma", Journal of the American Academy of Dermatology, Vol.14, No.5, 1986, pp. 857-860. [6] A. F. Jerant, J. T. Johnson, C. Demastes Sheridan & T. J. Caffrey, "Early detection and treatment of Skin cancer" , American family physician, Vol. 62, No.2, 2000, pp. 357-373. [7] C. Barata, J. S. Marques, & J. Rozeira, "Evaluation of color based keypoints and features for the classification of melanomas using the bag-offeatures model", In International Symposium on Visual Computing , Springer, Berlin, Heidelberg, Vol. 8033, 2013, pp. 40- 49. [8] J. C. Kavitha, A. Suruliandi, D. Nagarajan, & T. Nadu,"Melanoma detection in dermoscopic images Using global and local feature extraction", International Journal of Multimedia and Ubiquitous Engineering, 12(5), 2017, pp. 19- 28. [9] N. S. Ramteke, & S. V. Jain, "ABCD rule based automatic computer-aided skin cancer detection using MATLAB" ,International Journal of Computer Technology and Applications, Vol.4, No.4, 2013, pp.691-705. [10] D. D. Miller, & E. W. Brown, "Artificial Intelligence in medical practice: the question to the answer?",The American journal of medicine, Vol.131, No.2, 2018,pp. 129-133. [11] M. A. Calin, S. V. Parasca, & D. Manea, "Comparison of spectral angle mapper and support vector machine classification methods for mapping skin burn using hyperspectral imaging", In Unconventional Optical Imaging. International Society for Optics and Photonics, Vol.10677 2018, pp. 106773. [12] H. R. Firmansyah, E. M. Kusumaningtyas, & F. F. Hardiansyah, "Detection melanoma cancer using ABCD rule based on mobile device", In 2017 International Electronics Symposium on knowledge Creation and Intelligent Computing (IES-KCIC) IEEE, 2017, pp. 127-131. [13] N. Garg, V. Sharma, & P. Kaur, "Melanoma Skin Cancer Detection Using Image Processing", In Sensors and Image Processing , Springer, Singapore, vol.651, 2018, pp. 111-119. [14] M. Monisha, A. Suresh, B. T. Bapu, & M. R. Rashmi , "Classification of malignant melanoma and benign skin lesion by using back propagation neural network and ABCD rule" ,ClusterComputing, 2018, pp.1-11. [15] C. Salem, D. Azar, & S. Tokajian, "An Image Processing and Genetic Algorithm-based Approach for the Detection of Melanoma in Patients", Methods of information in medicine, Vol.57, No. 1, 2018, pp.74-80. [16] D. Roffman, G. Hart, M., Ko Girardi, C. J., & J.Deng, "Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network", Scientific reports, Vol. 8, No.1, 2018, pp.1701-1716. [17] D. Zhao, H. Liu, Y. Zheng, He, Y., Lu, D., & C. Lyu, "A reliable method for colorectal cancer prediction based on feature selection and support vector machine", Medical & biological engineering & computing, 2018, pp.1-12.