Pigmented Skin Lesion Diagnosis by Automated Imaging System
- *Corresponding Author:
- Mai S. Mabrouk
Biomedical Engineering Department
MUST University, Giza, Egypt
Tel:+20 2 35676105
E-mail:[email protected] com
Received Date: September 15, 2015; Accepted Date: October 16, 2015; Published Date: November 30, 2015
Citation:Sheha MA, Mabrouk MS, Sharawy Amr (2015) Pigmented Skin Lesion Diagnosis by Automated Imaging System. J Bioengineer & Biomedical Sci 5:170. doi:10.4172/2155-9538.1000170
Copyright: © 2015 Sheha MA, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Pigmented skin lesions are the normal part of the skin, however its anomalous appearance is an annoying sign due to the presence of melanomas one of its malignant forms. Although melanoma is a deadly considerable disease, its early detection is a serious step toward mortality reduction. The proposed research discusses the outcome of introducing312 different features in a non-invasive diagnosis method for pigmented skin lesion diagnosis. To obviate the problem of qualitative interpretation, two different image sets are utilized to examine the proposed system, a set of images acquired by standard camera (clinical images) and another set of dermoscopic images captured from the magnified dermoscope. System contribution appears in using large conclusive set of features fed to different classifiers composing totally complete, new and different approaches for the purpose of disease diagnosis. Miscellaneous types of features used such as geometric, chromatic, and texture features extracted from the region of interest resulted from segmentation process. Then, the most prominent features that can cause an effect are selected by three different methods; Fisher score method, t-test, and F-test. The high-ranking features are used for the diagnosis of the two lesion groups using Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) as three different classifiers proposed. System performance was measured in regards Specificity, Sensitivity and Accuracy. The ANN designed with the feature selected according to fisher score method enables a diagnostic accuracy of 96. 25% and 97% for dermoscopic and clinical images respectively.