Abstract
Early detection of skin cancer through improved techniques and innovative technologies has the greatest potential for significantly reducing both morbidity and mortality associated with this disease. In this paper, an effective framework of a CAD (Computer-Aided Diagnosis) system for melanoma skin cancer is developed mainly by application of an SVM (Support Vector Machine) model on an optimized set of HOG (Histogram of Oriented Gradient) based descriptors of skin lesions. Experimental results obtained by applying the presented methodology on a large, publicly accessible dataset of dermoscopy images demonstrate that the proposed framework is a strong contender for the state-of-the-art alternatives by achieving high levels of sensitivity, specificity, and accuracy (98.21%, 96.43% and 97.32%, respectively), without sacrificing computational soundness.
Highlights
IntroductionDifferential diagnosis of melanoma from melanocytic nevi is not straightforward and is often considered as clinically challenging for skin cancer specialists, especially in the early stages
The National Cancer Institute (NCI) reported that melanoma is the most common form of cancer in adults ages 25 to 29
The findings suggests histogram equalization such as power constrained contrast enhancement, dynamic range compression, edge enhancement, noise re that among all the physical characteristics of melanoma
Summary
Differential diagnosis of melanoma from melanocytic nevi is not straightforward and is often considered as clinically challenging for skin cancer specialists, especially in the early stages. Even when using dermoscopy for diagnosis, the accuracy of melanoma diagnosis by expert dermatologists [3] is estimated to be within no more than 75%–84%, which is still considered to be rather far from satisfaction for diagnostic purposes This greatly motivates the recent growing interest in diagnostics techniques for computer-assisted analysis of lesion images, which can instead be efficiently and effectively applied. Perusal of the general clinical literature reveals that numerous algorithms and methodologies have been proposed by medical researchers and clinicians for the classification of malignant lesions using dermoscopy images.
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