Hybridization of CNN with LBP for Classification of Melanoma Images

被引:3
|
作者
Iqbal, Saeed [1 ]
Qureshi, Adnan N. [1 ]
Mustafa, Ghulam [2 ]
机构
[1] Univ Cent Punjab, Fac Informat Technol, Lahore, Pakistan
[2] Bahria Univ, Dept Comp Sci, Lahore Campus, Lahore, Pakistan
来源
CMC-COMPUTERS MATERIALS & CONTINUA | 2022年 / 71卷 / 03期
关键词
Skin cancer; convolutional neural network; feature extraction; local binary pattern; classification; DECISION-SUPPORT-SYSTEM; SKIN-CANCER; DERMOSCOPY IMAGES; DIAGNOSIS; LESIONS;
D O I
10.32604/cmc.2022.023178
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Skin cancer (melanoma) is one of the most aggressive of the cancers and the prevalence has significantly increased due to increased exposure to ultraviolet radiation. Therefore, timely detection and management of the lesion is a critical consideration in order to improve lifestyle and reduce mortality. To this end, we have designed, implemented and analyzed a hybrid approach entailing convolutional neural networks (CNN) and local binary patterns (LBP). The experiments have been performed on publicly accessible datasets ISIC 2017, 2018 and 2019 (HAM10000) with data augmentation for in-distribution generalization. As a novel contribution, the CNN architecture is enhanced with an intelligible layer, LBP, that extracts the pertinent visual patterns. Classification of Basal Cell Carcinoma, Actinic Keratosis, Melanoma and Squamous Cell Carcinoma has been evaluated on 8035 and 3494 cases for training and testing, respectively. Experimental outcomes with cross-validation depict a plausible performance with an average accuracy of 97.29%, sensitivity of 95.63% and specificity of 97.90%. Hence, the proposed approach can be used in research and clinical settings to provide second opinions, closely approximating experts' intuition.
引用
收藏
页码:4915 / 4939
页数:25
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