Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model
Internet of Things (IoT)-assisted skin cancer recognition integrates several connected devices and sensors for supporting the primary analysis and monitoring of skin conditions. A preliminary analysis of skin cancer images is extremely difficult because of factors such as distinct sizes and shapes o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/15/20/5016 |
| _version_ | 1797574396003483648 |
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| author | Marwa Obayya Munya A. Arasi Nabil Sharaf Almalki Saud S. Alotaibi Mutasim Al Sadig Ahmed Sayed |
| author_facet | Marwa Obayya Munya A. Arasi Nabil Sharaf Almalki Saud S. Alotaibi Mutasim Al Sadig Ahmed Sayed |
| author_sort | Marwa Obayya |
| collection | DOAJ |
| description | Internet of Things (IoT)-assisted skin cancer recognition integrates several connected devices and sensors for supporting the primary analysis and monitoring of skin conditions. A preliminary analysis of skin cancer images is extremely difficult because of factors such as distinct sizes and shapes of lesions, differences in color illumination, and light reflections on the skin surface. In recent times, IoT-based skin cancer recognition utilizing deep learning (DL) has been used for enhancing the early analysis and monitoring of skin cancer. This article presents an optimal deep learning-based skin cancer detection and classification (ODL-SCDC) methodology in the IoT environment. The goal of the ODL-SCDC technique is to exploit metaheuristic-based hyperparameter selection approaches with a DL model for skin cancer classification. The ODL-SCDC methodology involves an arithmetic optimization algorithm (AOA) with the EfficientNet model for feature extraction. For skin cancer detection, a stacked denoising autoencoder (SDAE) classification model has been used. Lastly, the dragonfly algorithm (DFA) is utilized for the optimal hyperparameter selection of the SDAE algorithm. The simulation validation of the ODL-SCDC methodology has been tested on a benchmark ISIC skin lesion database. The extensive outcomes reported a better solution of the ODL-SCDC methodology compared with other models, with a maximum sensitivity of 97.74%, specificity of 99.71%, and accuracy of 99.55%. The proposed model can assist medical professionals, specifically dermatologists and potentially other healthcare practitioners, in the skin cancer diagnosis process. |
| first_indexed | 2024-03-10T21:22:02Z |
| format | Article |
| id | doaj.art-dc900e894f1646d1839b66af5e96ee4a |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-10T21:22:02Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-dc900e894f1646d1839b66af5e96ee4a2023-11-19T15:59:22ZengMDPI AGCancers2072-66942023-10-011520501610.3390/cancers15205016Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning ModelMarwa Obayya0Munya A. Arasi1Nabil Sharaf Almalki2Saud S. Alotaibi3Mutasim Al Sadig4Ahmed Sayed5Department of Biomedical Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Science, College of Science and Arts in RijalAlmaa, King Khalid University, Abha 62529, Saudi ArabiaDepartment of Special Education, College of Education, King Saud University, Riyadh 12372, Saudi ArabiaDepartment of Information Systems, College of Computing and Information System, Umm Al-Qura University, Mecca 21421, Saudi ArabiaDepartment of Computer Science, College of Science, Majmaah University, Al Majmaah 11952, Saudi ArabiaResearch Center, Future University in Egypt, New Cairo 11835, EgyptInternet of Things (IoT)-assisted skin cancer recognition integrates several connected devices and sensors for supporting the primary analysis and monitoring of skin conditions. A preliminary analysis of skin cancer images is extremely difficult because of factors such as distinct sizes and shapes of lesions, differences in color illumination, and light reflections on the skin surface. In recent times, IoT-based skin cancer recognition utilizing deep learning (DL) has been used for enhancing the early analysis and monitoring of skin cancer. This article presents an optimal deep learning-based skin cancer detection and classification (ODL-SCDC) methodology in the IoT environment. The goal of the ODL-SCDC technique is to exploit metaheuristic-based hyperparameter selection approaches with a DL model for skin cancer classification. The ODL-SCDC methodology involves an arithmetic optimization algorithm (AOA) with the EfficientNet model for feature extraction. For skin cancer detection, a stacked denoising autoencoder (SDAE) classification model has been used. Lastly, the dragonfly algorithm (DFA) is utilized for the optimal hyperparameter selection of the SDAE algorithm. The simulation validation of the ODL-SCDC methodology has been tested on a benchmark ISIC skin lesion database. The extensive outcomes reported a better solution of the ODL-SCDC methodology compared with other models, with a maximum sensitivity of 97.74%, specificity of 99.71%, and accuracy of 99.55%. The proposed model can assist medical professionals, specifically dermatologists and potentially other healthcare practitioners, in the skin cancer diagnosis process.https://www.mdpi.com/2072-6694/15/20/5016Internet of Thingsskin cancer diagnosisdermoscopic imagesdeep learningmetaheuristics |
| spellingShingle | Marwa Obayya Munya A. Arasi Nabil Sharaf Almalki Saud S. Alotaibi Mutasim Al Sadig Ahmed Sayed Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model Cancers Internet of Things skin cancer diagnosis dermoscopic images deep learning metaheuristics |
| title | Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model |
| title_full | Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model |
| title_fullStr | Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model |
| title_full_unstemmed | Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model |
| title_short | Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model |
| title_sort | internet of things assisted smart skin cancer detection using metaheuristics with deep learning model |
| topic | Internet of Things skin cancer diagnosis dermoscopic images deep learning metaheuristics |
| url | https://www.mdpi.com/2072-6694/15/20/5016 |
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