Classification of dermoscopy images using naïve Bayesian and decision tree techniques

Arasi, Munya A.; El-Sayed M. El-Horbaty; Salem A.; El-Dahshan, El-sayed;

Abstract


Malignant melanoma is one of the most dangerous types of skin cancers, which may grow on any part of the body. Medical Informatics utilized computer technology such as Computer Aided Diagnosis (CAD) to diagnose the disease. Many researchers developed CAD systems for melanoma diagnosis. Early diagnosis of melanoma is a main strategy to reduce melanoma-related deaths. This paper presents intelligence techniques namely, Naïve Bayes and Decision Tree to diagnose malignant melanoma. Dermoscopy images are taken from Dermatology Information System (DermIS) and DermQuest, image enhancement is achieved by various pre-processing techniques. The extracted features are based on hybrid Discrete Wavelet Transform (DWT) and Principle Component Analysis (PCA), and texture features. These features become the input to the various classification techniques like Naïve Bayes and Decision Tree to classify the lesions as malignant or benign. The results show that rate of accuracy of using hybrid DWT and PCA with Decision Tree is 92.86%, while Naive Bayes gives a higher rate of accuracy of about 98.8%. The results indicate that the Naive Bayes is better than decision tree, because it has shown excellent diagnostic accuracy; also the results show that the hybrid DWT and PCA features are more effective in improving the accuracy than the texture features for melanoma diagnosis. The comparative results indicated that the proposed techniques have excellent accuracy than the other techniques in this field of melanoma diagnosis.


Other data

Title Classification of dermoscopy images using naïve Bayesian and decision tree techniques
Authors Arasi, Munya A.; El-Sayed M. El-Horbaty ; Salem A. ; El-Dahshan, El-sayed 
Keywords Computer-Aided-Diagnosis;Decision-Tree;DWT;Malignant-melanoma;Medical-Informatics;Naive-Bayes;PCA
Issue Date 12-Feb-2019
Publisher IEEE
Conference 2018 1st Annual International Conference on Information and Sciences, AiCIS 2018
ISBN 9781538691885
DOI 10.1109/AiCIS.2018.00015
Scopus ID 2-s2.0-85063106423
Web of science ID WOS:000462987700002

Recommend this item

Similar Items from Core Recommender Database

Google ScholarTM

Check

Citations 14 in scopus


Items in Ain Shams Scholar are protected by copyright, with all rights reserved, unless otherwise indicated.