Hybridization of CNN with LBP for Classification of Melanoma Images

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.

Automated summary

Skin cancer (melanoma), with its high aggressiveness and increased prevalence due to ultraviolet radiation, requires timely detection and management. This study proposes a hybrid approach using convolutional neural networks (CNN) and local binary patterns (LBP) to improve classification accuracy. The experimental results show promising performance in distinguishing different types of skin cancers, providing a valuable tool for research and clinical settings.