Authors:
Jinen Daghrir
1
;
2
;
Lotfi Tlig
2
;
Moez Bouchouicha
3
;
Noureddine Litaiem
4
;
Faten Zeglaoui
4
and
Mounir Sayadi
2
Affiliations:
1
Université de Sousse, ISITCom, 4011, Hammam Sousse, Tunisia
;
2
Université de Tunis, ENSIT, Laboratory SIME, Tunisia
;
3
Aix Marseille Univ, Université de Toulon, CNRS, LIS, Toulon, France
;
4
University of Tunis El-Manar, Faculty of Medicine of Tunis, Department of Dermatology, Charles Nicolle Hospital, Tunis, Tunisia
Keyword(s):
Melanoma Inspection, Medical Imaging, Color Name Extraction, Machine Learning, Computer Vision, Image Processing.
Abstract:
Digital imaging is widely used for creating automated systems for medical purposes such as the diagnosis of certain kinds of diseases. One typical use of these computer vision diagnosis systems in dermatology is the inspection of melanoma skin cancer, which is one of the most fatal skin cancer. For the early detection of melanoma, a lot of systems have been proposed. Most of them use some visual features through image processing methods, such as color processing and border and texture inspection. Color variation is a good clue to differentiate melanoma and benign lesions. Thus, it is important to process skin lesion images to extract the various colors. The paper presents a new method that extracts the different color names from a skin lesion in a supervised way based on observed skin condition types. These features can ensure accurate melanoma detection with other types of features. To demonstrate the effectiveness of our suggested representation, we construct a prediction system fo
r inspecting the malignancy of skin lesions. The experimental results show a consistent improvement in the prediction performance against other color representations.
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