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.
Highlights
It has been observed that cancer rates are increasing rapidly due to life-style of people; there are different type of cancers, like Actinic Keratoses, Basal cell carcinoma, Squamous cell carcinoma and Melanoma
The main theme of this paper is to classify skin lesion images using convolutional neural network and classify according to Skin Cancer types (Basel Cell Carcinoma (BCC), Actinic Keratoses (AK), Melanoma and Squamous Cell Carcinoma (SCC))
The results are obtained from 11529 images (which are downloaded from ISIC dataset (Combination of ISIC 2017, 2018 and 2019 (HAM10000)–327 of Actinic Keratoses, 379 of Basel Cell Carcinoma (BCC) and 153 of Squamous Cell Carcinoma (SCC)))
Summary
The skin being largest organ of the human body performs critical tasks of providing protective barrier against mechanical, thermal and physical injury, exposure to hazardous substances and light and maintaining body temperature. It has been observed that cancer rates are increasing rapidly due to life-style of people; there are different type of cancers, like Actinic Keratoses, Basal cell carcinoma, Squamous cell carcinoma and Melanoma. Detection of these cancers is curable and can save life. Automatic system for diagnosis melanoma is proposed by Isasi et al [20] and an Android application is developed by Ramlakhan et al [21] for its classification They have developed an intelligent system using convolutional neural network to classify the melanoma
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
