Open Access

ARTICLE

Hybridization of CNN with LBP for Classification of Melanoma Images

Saeed Iqbal^1,*, Adnan N. Qureshi¹, Ghulam Mustafa²
1 Faculty of Information Technology, University of Central Punjab, Lahore, Pakistan
2 Department of Computer Sciences, Bahria University Lahore Campus, Pakistan
* Corresponding Author: Saeed Iqbal. Email:

Computers, Materials & Continua 2022, 71(3), 4915-4939. https://doi.org/10.32604/cmc.2022.023178

Received 30 August 2021; Accepted 14 October 2021; Issue published 14 January 2022

Abstract

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.

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Keywords

Skin cancer; convolutional neural network; feature extraction; local binary pattern; classification

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