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https://doi.org/10.2498/cit.2005.01.06

An Accelerated System for Melanoma Diagnosis Based on Subset Feature Selection

Ezzeddine Zagrouba
Walid Barhoumi

Puni tekst: engleski, pdf (466 KB) str. 69-82 preuzimanja: 990* citiraj
APA 6th Edition
Zagrouba, E. i Barhoumi, W. (2005). An Accelerated System for Melanoma Diagnosis Based on Subset Feature Selection. Journal of computing and information technology, 13 (1), 69-82. https://doi.org/10.2498/cit.2005.01.06
MLA 8th Edition
Zagrouba, Ezzeddine i Walid Barhoumi. "An Accelerated System for Melanoma Diagnosis Based on Subset Feature Selection." Journal of computing and information technology, vol. 13, br. 1, 2005, str. 69-82. https://doi.org/10.2498/cit.2005.01.06. Citirano 07.03.2021.
Chicago 17th Edition
Zagrouba, Ezzeddine i Walid Barhoumi. "An Accelerated System for Melanoma Diagnosis Based on Subset Feature Selection." Journal of computing and information technology 13, br. 1 (2005): 69-82. https://doi.org/10.2498/cit.2005.01.06
Harvard
Zagrouba, E., i Barhoumi, W. (2005). 'An Accelerated System for Melanoma Diagnosis Based on Subset Feature Selection', Journal of computing and information technology, 13(1), str. 69-82. https://doi.org/10.2498/cit.2005.01.06
Vancouver
Zagrouba E, Barhoumi W. An Accelerated System for Melanoma Diagnosis Based on Subset Feature Selection. Journal of computing and information technology [Internet]. 2005 [pristupljeno 07.03.2021.];13(1):69-82. https://doi.org/10.2498/cit.2005.01.06
IEEE
E. Zagrouba i W. Barhoumi, "An Accelerated System for Melanoma Diagnosis Based on Subset Feature Selection", Journal of computing and information technology, vol.13, br. 1, str. 69-82, 2005. [Online]. https://doi.org/10.2498/cit.2005.01.06

Sažetak
In this paper we present an optimised system for
diagnosing skin lesions based on digitized dermatoscopic color images.
This system is composed mainly of three levels : lesion detection, lesion description (features
selection) and decision.
The preprocessing of the lesion image
is used to remove the undesired objects from the original image
and the extraction of the lesion is done by separating it from the healthy surrounding skin.
The classification scheme is based on the extraction of a set of features
modeling clinical signs of malignancy.
The produced vector of features scores is used as input to
a multi-layer perceptron classifier in order to assign the lesion
to the class of benign lesions or to the one of malignant melanomas.
We focus particularly in this paper on the critical step of
the features selection
allowing to select a reasonable
reduced number of useful features while removing
redundant information and approximating the properties of melanoma recognition.
This permits to reduce
the dimension of the lesion's vector, and consequently the calculation time,
without a significant loss of information.
In fact, a large set of features was investigated
by the application of relevant features selection techniques.
Then, the number of features for
classification was optimized and only five well-selected features were used
to cover the discriminatory information about lesions malignancy.
With this approach, for reasonably balanced training/test sets, we record
a good classification rate of 77.7% in a very promising cpu time.

Hrčak ID: 44703

URI
https://hrcak.srce.hr/44703

Posjeta: 1.460 *