Melanoma Skin Cancer Recognition with a Convolutional Neural Network and Feature Dimensions Reduction with Aquila Optimizer
<b>Background:</b> Melanoma is a highly aggressive form of skin cancer, necessitating early and accurate detection for effective treatment. This study aims to develop a novel classification system for melanoma detection that integrates Convolutional Neural Networks (CNNs) for feature ext...
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MDPI AG
2025-03-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/6/761 |
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| author | Jalaleddin Mohamed Necmi Serkan Tezel Javad Rahebi Raheleh Ghadami |
| author_facet | Jalaleddin Mohamed Necmi Serkan Tezel Javad Rahebi Raheleh Ghadami |
| author_sort | Jalaleddin Mohamed |
| collection | DOAJ |
| description | <b>Background:</b> Melanoma is a highly aggressive form of skin cancer, necessitating early and accurate detection for effective treatment. This study aims to develop a novel classification system for melanoma detection that integrates Convolutional Neural Networks (CNNs) for feature extraction and the Aquila Optimizer (AO) for feature dimension reduction, improving both computational efficiency and classification accuracy. <b>Methods:</b> The proposed method utilized CNNs to extract features from melanoma images, while the AO was employed to reduce feature dimensionality, enhancing the performance of the model. The effectiveness of this hybrid approach was evaluated on three publicly available datasets: ISIC 2019, ISBI 2016, and ISBI 2017. <b>Results:</b> For the ISIC 2019 dataset, the model achieved 97.46% sensitivity, 98.89% specificity, 98.42% accuracy, 97.91% precision, 97.68% F1-score, and 99.12% AUC-ROC. On the ISBI 2016 dataset, it reached 98.45% sensitivity, 98.24% specificity, 97.22% accuracy, 97.84% precision, 97.62% F1-score, and 98.97% AUC-ROC. For ISBI 2017, the results were 98.44% sensitivity, 98.86% specificity, 97.96% accuracy, 98.12% precision, 97.88% F1-score, and 99.03% AUC-ROC. The proposed method outperforms existing advanced techniques, with a 4.2% higher accuracy, a 6.2% improvement in sensitivity, and a 5.8% increase in specificity. Additionally, the AO reduced computational complexity by up to 37.5%. <b>Conclusions:</b> The deep learning-Aquila Optimizer (DL-AO) framework offers a highly efficient and accurate approach for melanoma detection, making it suitable for deployment in resource-constrained environments such as mobile and edge computing platforms. The integration of DL with metaheuristic optimization significantly enhances accuracy, robustness, and computational efficiency in melanoma detection. |
| format | Article |
| id | doaj-art-427f8ad6a0774bc897cc32fb09cd384a |
| institution | DOAJ |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Diagnostics |
| spelling | doaj-art-427f8ad6a0774bc897cc32fb09cd384a2025-08-20T02:42:40ZengMDPI AGDiagnostics2075-44182025-03-0115676110.3390/diagnostics15060761Melanoma Skin Cancer Recognition with a Convolutional Neural Network and Feature Dimensions Reduction with Aquila OptimizerJalaleddin Mohamed0Necmi Serkan Tezel1Javad Rahebi2Raheleh Ghadami3Electrical and Electronics Engineering Department, Karabuk University, 78050 Karabuk, TürkiyeElectrical and Electronics Engineering Department, Karabuk University, 78050 Karabuk, TürkiyeDepartment of Software Engineering, Istanbul Topkapi University, 34662 Istanbul, TürkiyeDepartment of Computer Engineering, Istanbul Topkapi University, 34662 Istanbul, Türkiye<b>Background:</b> Melanoma is a highly aggressive form of skin cancer, necessitating early and accurate detection for effective treatment. This study aims to develop a novel classification system for melanoma detection that integrates Convolutional Neural Networks (CNNs) for feature extraction and the Aquila Optimizer (AO) for feature dimension reduction, improving both computational efficiency and classification accuracy. <b>Methods:</b> The proposed method utilized CNNs to extract features from melanoma images, while the AO was employed to reduce feature dimensionality, enhancing the performance of the model. The effectiveness of this hybrid approach was evaluated on three publicly available datasets: ISIC 2019, ISBI 2016, and ISBI 2017. <b>Results:</b> For the ISIC 2019 dataset, the model achieved 97.46% sensitivity, 98.89% specificity, 98.42% accuracy, 97.91% precision, 97.68% F1-score, and 99.12% AUC-ROC. On the ISBI 2016 dataset, it reached 98.45% sensitivity, 98.24% specificity, 97.22% accuracy, 97.84% precision, 97.62% F1-score, and 98.97% AUC-ROC. For ISBI 2017, the results were 98.44% sensitivity, 98.86% specificity, 97.96% accuracy, 98.12% precision, 97.88% F1-score, and 99.03% AUC-ROC. The proposed method outperforms existing advanced techniques, with a 4.2% higher accuracy, a 6.2% improvement in sensitivity, and a 5.8% increase in specificity. Additionally, the AO reduced computational complexity by up to 37.5%. <b>Conclusions:</b> The deep learning-Aquila Optimizer (DL-AO) framework offers a highly efficient and accurate approach for melanoma detection, making it suitable for deployment in resource-constrained environments such as mobile and edge computing platforms. The integration of DL with metaheuristic optimization significantly enhances accuracy, robustness, and computational efficiency in melanoma detection.https://www.mdpi.com/2075-4418/15/6/761Aquila Optimizerconvolutional neural networkfeature dimensions reductionmelanoma skin cancer |
| spellingShingle | Jalaleddin Mohamed Necmi Serkan Tezel Javad Rahebi Raheleh Ghadami Melanoma Skin Cancer Recognition with a Convolutional Neural Network and Feature Dimensions Reduction with Aquila Optimizer Diagnostics Aquila Optimizer convolutional neural network feature dimensions reduction melanoma skin cancer |
| title | Melanoma Skin Cancer Recognition with a Convolutional Neural Network and Feature Dimensions Reduction with Aquila Optimizer |
| title_full | Melanoma Skin Cancer Recognition with a Convolutional Neural Network and Feature Dimensions Reduction with Aquila Optimizer |
| title_fullStr | Melanoma Skin Cancer Recognition with a Convolutional Neural Network and Feature Dimensions Reduction with Aquila Optimizer |
| title_full_unstemmed | Melanoma Skin Cancer Recognition with a Convolutional Neural Network and Feature Dimensions Reduction with Aquila Optimizer |
| title_short | Melanoma Skin Cancer Recognition with a Convolutional Neural Network and Feature Dimensions Reduction with Aquila Optimizer |
| title_sort | melanoma skin cancer recognition with a convolutional neural network and feature dimensions reduction with aquila optimizer |
| topic | Aquila Optimizer convolutional neural network feature dimensions reduction melanoma skin cancer |
| url | https://www.mdpi.com/2075-4418/15/6/761 |
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