Accuracy Assessment & Classification of Keratosis Skin Lesion Images using Feature Extraction & Classification Algorithms-LBP, LDP& HOG
Manjunath Rao1, Calvin Joshua Fernandez2, Sreekumar K3
1Manjunath Rao*, Department of Computer Science and IT, Amrita School of Arts and Sciences, Kochi, Amrita Vishwa Vidyapeetham, India.
2Calvin Joshua Fernandez, Department of Computer Science and IT, Amrita School of Arts and Sciences, Kochi, Amrita Vishwa Vidyapeetham, India.
3Sreekumar K, Asst. Professor of Department of Computer Science and IT, Amrita School of Arts and Sciences, Kochi, Amrita Vishwa Vidyapeetham, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 28, 2020. | Manuscript published on April 10, 2020. | PP: 397-403 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3737049620/2020©BEIESP | DOI: 10.35940/ijitee.F3737.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: There are hundreds of human-affected skin diseases. The most severe skin disorders may have identical symptoms, so recognizing the distinctions between them is crucial. People should work closely with a dermatologist to identify and manage every skin disorder and insure it does not impact their lifestyle. Actinic keratosis (AK), that is also classified as solar or senile keratosis; is a pre-malignant crusty, thick skin area. It is a disorder of epidermal keratinocytes, induced by UV radiation upon the skin. While pre-cancerous in nature, they can develop into a form of skin cancer called carcinoma if left unaddressed. The other type of keratosis dealt within this paper is seborrheic keratosis, which are brown or black, thick, wart-like, waxy oval-shaped, slightly raised skin surfaces. The growths aren’t damaging. Nevertheless, in some instances it can be impossible to differentiate a seborrheic keratosis from melanoma, which is a very dangerous form of skin cancer. Nevus (or moles) skin lesions are ones which are benign, where it may very rarely turn into melanoma skin cancer. In this article, along with techniques for extracting features (LDP [Local Directional Patterns], LBP [Local Binary Patterns] and HOG [Histogram of Oriented Gradients]),we have used an SVM classifier for the classification of Keratosis and also nevus skin photos. The LBP, LDP and HOG are means to extract features; these images are subsequently used for identification of derived features from these methods or algorithms and classified by the SVM (Support Vector Machine) classifier. For many of the classifications of keratosis and nevus skin images using these algorithms, we have obtained accuracy nearly above 80 %, whereby the LBP system together with the SVM classifier was the most powerful attribute extraction tool of the three with their polynomial kernel type. Using this algorithm-classifier, the main AK and nevus skin lesion images can be detected and diagnosed by the doctors in its early stage itself, thus helping save lives.
Keywords: Keratosis, Actinic, Seborrheic, Nevus, Lesions, Benign, p Re-malignant, Feature Extraction ,SVM, LBP, LDP, Classification.
Scope of the Article: VLSI Algorithms