A Comparative Study of Various Techniques for Skin Cancer Detection

Abstract
Authors
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References

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.

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