Melanoma Cancer Diagnosis Device Using Image Processing Techniques
Mohd Afizi Mohd Shukran1, Nor Suraya Mariam Ahmad2, Suzaimah Ramli3, Farhana Rahmat4
1Mohd Afizi Mohd Shukran, Faculty of Defence Science and Technology National Defence University of Malaysia, Kuala Lumpur, Malaysia.
2Nor Suraya Mariam Ahmad, Faculty of Defence Science and Technology National Defence University of Malaysia, Kuala Lumpur, Malaysia.
3Suzaimah Ramli, Faculty of Defence Science and Technology National Defence University of Malaysia, Kuala Lumpur, Malaysia.
4Farhana Rahmat, Faculty of Medicine and Defense Health National Defence University of Malaysia, Kuala Lumpur, Malaysia.
Manuscript received on 15 February 2019 | Revised Manuscript received on 06 March 2019 | Manuscript Published on 08 June 2019 | PP: 490-494 | Volume-7 Issue-5S4, February 2019 | Retrieval Number: E11040275S419/19©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Melanoma is well-known skin cancer that cause fatal. Therefore, detection of melanoma at early stage are essential to enhance the successful of survival rate. For the detection of melanoma, proper analysis is carried out on the skin lesion according to a set of specific clinical characteristics. This skin lesion clinically diagnosed begin with primary clinical screening and dermoscopic analysis, a biopsy and histopathological examination. Lastly, this skin lesion is classified as either “potential melanoma” or “non-melanoma”. The process involved are lengthy to the patient and painful. Nevertheless, it can be reducing by automated skin cancer diagnosis base on skin lesions images classification. Automated classification of skin lesions using images is usually challenging, where it is needed to solve multiple task. The input to this tool is the skin lesion images, next apply image processing techniques, and later on this skin lesion images are analyses to conclude occurrence of melanoma. Typically, the analysis to checks for the various Melanoma are using pre-defined thresh-olds in classification stage such as Asymmetry, Border, Colour, Diameter and Evolution (ABCDE) where color, texture, size and shape are being analysis for image segmentation and feature stages. Within the Feature Extraction stage the Feature Values Extracted are being compared and the skin lesion is classified as Melanoma or Normal skin. For most of the skin images, this particular classification method proves to be efficient. This paper intends to provide useful information and methods that been use in skin cancer diagnosis. Hence, it gives good start for researchers to understand automated skin cancer detection at basic level phase.
Keywords: ABCDE and Feature Extraction, Image Processing, Melanoma.
Scope of the Article: Image analysis and Processing