Pre-processing of automatic skin cancer detection system: Comparative study

Azadeh, AN; Jumaily, AA; Hoshyar, AN

Pre-processing of automatic skin cancer detection system: Comparative study

Azadeh, AN Jumaily, AA

Hoshyar, AN

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Publication Type:: Journal Article
Citation:: International Journal on Smart Sensing and Intelligent Systems, 2014, 7 (3), pp. 1364 - 1377
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Azadeh, AN	en_US
dc.contributor.author	Jumaily, AA https://orcid.org/0000-0003-0297-2463	en_US
dc.contributor.author	Hoshyar, AN	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	International Journal on Smart Sensing and Intelligent Systems, 2014, 7 (3), pp. 1364 - 1377	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35510
dc.description.abstract	Skin cancer is increasing and effect many people in different part of the world. Malignant melanoma as the deadliest type of skin cancer can be treated successfully if it detected early. Automatic detection is one of the most challenging research areas that can be used for early detection of such vital cancer. Over the last few years, many automatic diagnosis systems been suggested by different researchers targeting increasing of the diagnosis accuracy. This paper presents a quick review on the design of whole system and focus in preprocessing step of the automatic system. Preprocessing as the basis of automation system plays a vital role for accurate detection. This paper implements three techniques of contrast enhancement in the framework of three methodologies to find out the most effective one for further processing. The quality of resulted images in each methodology has been found based on testing the skin cancer images database using three image quality measurements.	en_US
dc.relation.ispartof	International Journal on Smart Sensing and Intelligent Systems	en_US
dc.title	Pre-processing of automatic skin cancer detection system: Comparative study	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	7	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1007 Nanotechnology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

Skin cancer is increasing and effect many people in different part of the world. Malignant melanoma as the deadliest type of skin cancer can be treated successfully if it detected early. Automatic detection is one of the most challenging research areas that can be used for early detection of such vital cancer. Over the last few years, many automatic diagnosis systems been suggested by different researchers targeting increasing of the diagnosis accuracy. This paper presents a quick review on the design of whole system and focus in preprocessing step of the automatic system. Preprocessing as the basis of automation system plays a vital role for accurate detection. This paper implements three techniques of contrast enhancement in the framework of three methodologies to find out the most effective one for further processing. The quality of resulted images in each methodology has been found based on testing the skin cancer images database using three image quality measurements.

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http://hdl.handle.net/10453/35510