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Performance Analysis of Low-Level and High-Level Intuitive Features for Melanoma Detection

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Department of Computer Systems Engineering, University of Engineering & Technology, Peshawar 54890, Pakistan
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Department of Electrical Engineering, CECOS University of IT & Emerging Sciences, Peshawar 25000, Pakistan
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Departmnt of Computer Science & IT, University of Engineering & Technology, Jalozai Campus, Khyber Pakhtunkhwa 24240, Pakistan
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Authors to whom correspondence should be addressed.
Electronics 2019, 8(6), 672; https://doi.org/10.3390/electronics8060672
Received: 27 May 2019 / Revised: 7 June 2019 / Accepted: 7 June 2019 / Published: 13 June 2019
(This article belongs to the Section Bioelectronics)
This paper presents an intelligent approach for the detection of Melanoma—a deadly skin cancer. The first step in this direction includes the extraction of the textural features of the skin lesion along with the color features. The extracted features are used to train the Multilayer Feed-Forward Artificial Neural Networks. We evaluate the trained networks for the classification of test samples. This work entails three sets of experiments including 50 % , 70 % and 90 % of the data used for training, while the remaining 50 % , 30 % , and 10 % constitute the test sets. Haralick’s statistical parameters are computed for the extraction of textural features from the lesion. Such parameters are based on the Gray Level Co-occurrence Matrices (GLCM) with an offset of 2 , 4 , 8 , 12 , 16 , 20 , 24 and 28, each with an angle of 0 , 45 , 90 and 135 degrees, respectively. In order to distill color features, we have calculated the mean, median and standard deviation of the three color planes of the region of interest. These features are fed to an Artificial Neural Network (ANN) for the detection of skin cancer. The combination of Haralick’s parameters and color features have proven better than considering the features alone. Experimentation based on another set of features such as Asymmetry, Border irregularity, Color and Diameter (ABCD) features usually observed by dermatologists has also been demonstrated. The ‘D’ feature is however modified and named Oblongness. This feature captures the ratio between the length and the width. Furthermore, the use of modified standard deviation coupled with ABCD features improves the detection of Melanoma by an accuracy of 93.7 % View Full-Text
Keywords: skin cancer detection; melanoma; GLCM; color features; ANN skin cancer detection; melanoma; GLCM; color features; ANN
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Ashfaq, M.; Minallah, N.; Ullah, Z.; Ahmad, A.M.; Saeed, A.; Hafeez, A. Performance Analysis of Low-Level and High-Level Intuitive Features for Melanoma Detection. Electronics 2019, 8, 672.

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