Leveraging convolutional neural networks and hashing techniques for the secure classification of monkeypox disease
Abstract The World Health Organization declared a state of emergency in 2022 because of monkeypox. This disease has raised international concern as it has spread beyond Africa, where it is endemic. The global community has shown attention and solidarity in combating this disease as its daily increas...
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-75030-y |
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| author | Essam Abdellatef Alshimaa H. Ismail M. I. Fath Allah Wafaa A. Shalaby |
| author_facet | Essam Abdellatef Alshimaa H. Ismail M. I. Fath Allah Wafaa A. Shalaby |
| author_sort | Essam Abdellatef |
| collection | DOAJ |
| description | Abstract The World Health Organization declared a state of emergency in 2022 because of monkeypox. This disease has raised international concern as it has spread beyond Africa, where it is endemic. The global community has shown attention and solidarity in combating this disease as its daily increase becomes evident. Various skin symptoms appear in people infected with this disease, which can spread easily, especially in a polluted environment. It is difficult to diagnose monkeypox in its early stages because of its similarity with the symptoms of other diseases such as chicken pox and measles. Recently, computer-aided classification methods such as deep learning and machine learning within artificial intelligence have been employed to detect various diseases, including COVID-19, tumor cells, and Monkeypox, in a short period and with high accuracy. In this study, we propose the CanDark model, an end-to-end deep-learning model that incorporates cancelable biometrics for diagnosing Monkeypox. CanDark stands for cancelable DarkNet-53, which means that DarkNet-53 CNN is utilized for extracting deep features from Monkeypox skin images. Then a cancelable method is applied to these features to protect patient information. Various cancelable techniques have been evaluated, such as bio-hashing, multilayer perceptron (MLP) hashing, index-of-maximum Gaussian random projection-based hashing (IoM-GRP), and index-of-maximum uniformly random permutation-based hashing (IoM-URP). The proposed approach’s performance is evaluated using various assessment issues such as accuracy, specificity, precision, recall, and fscore. Using the IoM-URP, the CanDark model is superior to other state-of-the-art Monkeypox diagnostic techniques. The proposed framework achieved an accuracy of 98.81%, a specificity of 98.73%, a precision of 98.9%, a recall of 97.02%, and fscore of 97.95%. |
| format | Article |
| id | doaj-art-aa6da6bdbdf64d8fb5d32a56bd1e6b2b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-aa6da6bdbdf64d8fb5d32a56bd1e6b2b2024-11-10T12:26:23ZengNature PortfolioScientific Reports2045-23222024-11-0114112510.1038/s41598-024-75030-yLeveraging convolutional neural networks and hashing techniques for the secure classification of monkeypox diseaseEssam Abdellatef0Alshimaa H. Ismail1M. I. Fath Allah2Wafaa A. Shalaby3Department of Electrical Engineering, Faculty of Engineering, Sinai UniversityInformation Technology Department, Faculty of Computer and Informatics, Tanta UniversityDepartment of Electrical Engineering, Faculty of Engineering, Suez UniversityDepartment of Electronic and Electrical Communication Engineering, Faculty of Electronic Engineering, Menoufia UniversityAbstract The World Health Organization declared a state of emergency in 2022 because of monkeypox. This disease has raised international concern as it has spread beyond Africa, where it is endemic. The global community has shown attention and solidarity in combating this disease as its daily increase becomes evident. Various skin symptoms appear in people infected with this disease, which can spread easily, especially in a polluted environment. It is difficult to diagnose monkeypox in its early stages because of its similarity with the symptoms of other diseases such as chicken pox and measles. Recently, computer-aided classification methods such as deep learning and machine learning within artificial intelligence have been employed to detect various diseases, including COVID-19, tumor cells, and Monkeypox, in a short period and with high accuracy. In this study, we propose the CanDark model, an end-to-end deep-learning model that incorporates cancelable biometrics for diagnosing Monkeypox. CanDark stands for cancelable DarkNet-53, which means that DarkNet-53 CNN is utilized for extracting deep features from Monkeypox skin images. Then a cancelable method is applied to these features to protect patient information. Various cancelable techniques have been evaluated, such as bio-hashing, multilayer perceptron (MLP) hashing, index-of-maximum Gaussian random projection-based hashing (IoM-GRP), and index-of-maximum uniformly random permutation-based hashing (IoM-URP). The proposed approach’s performance is evaluated using various assessment issues such as accuracy, specificity, precision, recall, and fscore. Using the IoM-URP, the CanDark model is superior to other state-of-the-art Monkeypox diagnostic techniques. The proposed framework achieved an accuracy of 98.81%, a specificity of 98.73%, a precision of 98.9%, a recall of 97.02%, and fscore of 97.95%.https://doi.org/10.1038/s41598-024-75030-yMonkeypoxCancelable techniquesCNNAnd DarkNet-53 |
| spellingShingle | Essam Abdellatef Alshimaa H. Ismail M. I. Fath Allah Wafaa A. Shalaby Leveraging convolutional neural networks and hashing techniques for the secure classification of monkeypox disease Scientific Reports Monkeypox Cancelable techniques CNN And DarkNet-53 |
| title | Leveraging convolutional neural networks and hashing techniques for the secure classification of monkeypox disease |
| title_full | Leveraging convolutional neural networks and hashing techniques for the secure classification of monkeypox disease |
| title_fullStr | Leveraging convolutional neural networks and hashing techniques for the secure classification of monkeypox disease |
| title_full_unstemmed | Leveraging convolutional neural networks and hashing techniques for the secure classification of monkeypox disease |
| title_short | Leveraging convolutional neural networks and hashing techniques for the secure classification of monkeypox disease |
| title_sort | leveraging convolutional neural networks and hashing techniques for the secure classification of monkeypox disease |
| topic | Monkeypox Cancelable techniques CNN And DarkNet-53 |
| url | https://doi.org/10.1038/s41598-024-75030-y |
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