A Comparative Study of Various Techniques used for Melanoma Detection

RAINA RAJU K, S. Swapna Kumar

Abstract


Skin cancer is one of the most fatal disease. It is easily curable, when it is detected in its beginning stage. Early detection of melanoma through accurate techniques and innovative technologies has the greatest potential for decreasing mortality associated with this disease. Mainly there are four steps for detecting melanoma which includes preprocessing, segmentation, feature extraction and classification. The preprocessing stage will remove all the artifacts associated with the lesion. The exact boundaries of lesion are identified from normal skin through segmentation method. Feature extraction stage is used for calculating and obtaining different parameters of the lesion region. The final stage is to classify the lesion as benign or malignant.  In this paper different types of segmentation methods and classification methods are described. Both of these stages are accurately implemented to reach the final detection of the lesion.

Full Text:

PDF

References


Ruchika Sharma, Dr. Pankaj Mohindru, Dr. Pooja, “Review of Segmentation Techniques for Melanoma Detection”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 6, Issue 7, July 2016.

D. Saranya, M. Malini, “A Review of Segmentation Techniques on Melanoma Detection” ,International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 4, 2015

Niket Amoda, Ramesh K Kulkarni, “Efficient Image Segmentation Using Watershed Transform”, InternatIonal Journal of Computer SCIenCe and technology, Vol. 4, Issue 2, AprIl - June 2013

M.H. Jafari, N. Karimi, E. Nasr-Esfahani, S. Samavi, S.M.R. Soroushmehr, K. Ward, K. Najarian, “Skin Lesion Segmentation in Clinical Images Using Deep Learning”, 23rd International Conference on Pattern Recognition (ICPR),IEEE 2016.

J. Premaladha1 , K. S. Ravichandran1, “Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms”, Springer, February 2016.

Adam Glowacz, Zygfryd Glowacz, “Recognition of images of finger skin with application of histogram,image filtration and K-NN classifier”, Biocybernetics and Biomedical engineering, December 2015




DOI: https://doi.org/10.23956/ijarcsse.v7i11.466

Refbacks

  • There are currently no refbacks.




© International Journals of Advanced Research in Computer Science and Software Engineering (IJARCSSE)| All Rights Reserved | Powered by Advance Academic Publisher.