Comparison of Deep Learning Algorithms for Early Detection of Melanoma Skin Cancer on Dermoscopic and Non-dermoscopic Images

Wamane, Niharika; Yadav, Amit Kumar; Bhoir, Jidnyasa; Shelke, Deep; Kadam, Deepali

doi:10.1007/978-3-031-36402-0_72

Lecture Notes in Computer Science

2023

DOI: 10.1007/978-3-031-36402-0_72

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Comparison of Deep Learning Algorithms for Early Detection of Melanoma Skin Cancer on Dermoscopic and Non-dermoscopic Images

Niharika Wamane

Amit Kumar Yadav

Jidnyasa Bhoir

et al.

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2024

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Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making

Saleh,

Hassan,

Salaheldin

2024

Sci Rep

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Skin cancer is a type of cancer disease in which abnormal alterations in skin characteristics can be detected. It can be treated if it is detected early. Many artificial intelligence-based models have been developed for skin cancer detection and classification. Considering the development of numerous models according to various scenarios and selecting the optimum model was rarely considered in previous works. This study aimed to develop various models for skin cancer classification and select the optimum model. Convolutional neural networks (CNNs) in the form of AlexNet, Inception V3, MobileNet V2, and ResNet 50 were used for feature extraction. Feature reduction was carried out using two algorithms of the grey wolf optimizer (GWO) in addition to using the original features. Skin cancer images were classified into four classes based on six machine learning (ML) classifiers. As a result, 51 models were developed with different combinations of CNN algorithms, without GWO algorithms, with two GWO algorithms, and with six ML classifiers. To select the optimum model with the best results, the multicriteria decision-making approach was utilized to rank the alternatives by perimeter similarity (RAPS). Model training and testing were conducted using the International Skin Imaging Collaboration (ISIC) 2017 dataset. Based on nine evaluation metrics and according to the RAPS method, the AlexNet algorithm with a classical GWO yielded the optimum model, achieving a classification accuracy of 94.5%. This work presents the first study on benchmarking skin cancer classification with many models. Feature reduction not only reduces the time spent on training but also improves classification accuracy. The RAPS method has proven its robustness in the problem of selecting the best model for skin cancer classification.

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Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making

Saleh,

Hassan,

Salaheldin

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Comparison of Deep Learning Algorithms for Early Detection of Melanoma Skin Cancer on Dermoscopic and Non-dermoscopic Images

Cited by 1 publication

References 8 publications

Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making

Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making

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