Design of Block-Scrambling-Based privacy protection mechanism in healthcare using fusion of transfer learning models with Hippopotamus optimization algorithm
Abstract In the human body, the skin is the main organ. Nearly 30–70% of individuals globally have skin-related health issues, for whom efficient and effective analysis is essential. A general method dermatologists use for analyzing skin illnesses is dermoscopy, which permits surveillance of the hid...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-04931-3 |
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| author | Ghada Moh. Samir Elhessewi Mohammed Yahya Alzahrani Mohammad Alamgeer Abdulbasit A. Darem Da’ad Albalawneh Mohammed Alqahtani Mutasim Al Sadig Sultan Alanazi |
| author_facet | Ghada Moh. Samir Elhessewi Mohammed Yahya Alzahrani Mohammad Alamgeer Abdulbasit A. Darem Da’ad Albalawneh Mohammed Alqahtani Mutasim Al Sadig Sultan Alanazi |
| author_sort | Ghada Moh. Samir Elhessewi |
| collection | DOAJ |
| description | Abstract In the human body, the skin is the main organ. Nearly 30–70% of individuals globally have skin-related health issues, for whom efficient and effective analysis is essential. A general method dermatologists use for analyzing skin illnesses is dermoscopy, which permits surveillance of the hidden structures of skin injuries, i.e., an area suffering from an illness whose effects are unseen to the naked eye. Dermoscopy is generally employed for cancers and other kinds of skin cancers with pigment. Yet, access to a dermoscopy is demanding in resource-poor areas and unnecessary for many general skin diseases. So, developing an effective skin disease analysis method that depends upon effortlessly accessible clinical imaging would be helpful and deliver lower-cost, common access to many individuals. Recently, computer-aided diagnosis (CAD) approaches have been effectively employed to detect skin cancers in dermatoscopic imaging. The CAD-based techniques will be beneficial for helping professionals detect and classify skin lesions. This paper presents an Advanced Skin Lesion Classification using Block-Scrambling-Based Encryption with a Fusion of Transfer Learning Models and a Hippopotamus Optimization (SLCBSBE-FTLHO) model. The main aim of the SLCBSBE-FTLHO model relies on automating the diagnostic procedures of skin lesions using optimal DL approaches. At first, the block-scrambling-based encryption (BSBE) technique is utilized in the image encryption pre-processing stage, and then the decryption process is performed. The feature extraction process employs the fusion of MobileNetV2, GoogLeNet, and AlexNet techniques. Furthermore, the conditional variational autoencoder (CVAE) method is implemented for skin lesion classification. To optimize the CVAE model performance, the hippopotamus optimization (HO) model is utilized for hyperparameter tuning to ensure that the optimum hyperparameters are chosen for enhanced accuracy. To exhibit the improved performance of the SLCBSBE-FTLHO approach, a comprehensive experimental analysis is conducted under the skin cancer ISIC dataset. The comparative study of the SLCBSBE-FTLHO approach portrayed a superior accuracy value of 99.48% over existing models. |
| format | Article |
| id | doaj-art-0d1f979df8a24ef3819baa48a7b879c4 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0d1f979df8a24ef3819baa48a7b879c42025-08-20T04:01:36ZengNature PortfolioScientific Reports2045-23222025-07-0115112310.1038/s41598-025-04931-3Design of Block-Scrambling-Based privacy protection mechanism in healthcare using fusion of transfer learning models with Hippopotamus optimization algorithmGhada Moh. Samir Elhessewi0Mohammed Yahya Alzahrani1Mohammad Alamgeer2Abdulbasit A. Darem3Da’ad Albalawneh4Mohammed Alqahtani5Mutasim Al Sadig6Sultan Alanazi7Department of Health Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman UniversityFaculty of Computing and Information, Al-Baha UniversityDepartment of Information Systems, Applied College at Mahayil, King Khalid UniversityCenter for Scientific Research and Entrepreneurship, Northern Border UniversityDepartment of Computer Science, University College in Umluj, University of TabukDepartment of Information System and Cyber Security, College of Computing and Information Technology, University of BishaDepartment of Computer Science, College of Science, Majmaah UniversityDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz UniversityAbstract In the human body, the skin is the main organ. Nearly 30–70% of individuals globally have skin-related health issues, for whom efficient and effective analysis is essential. A general method dermatologists use for analyzing skin illnesses is dermoscopy, which permits surveillance of the hidden structures of skin injuries, i.e., an area suffering from an illness whose effects are unseen to the naked eye. Dermoscopy is generally employed for cancers and other kinds of skin cancers with pigment. Yet, access to a dermoscopy is demanding in resource-poor areas and unnecessary for many general skin diseases. So, developing an effective skin disease analysis method that depends upon effortlessly accessible clinical imaging would be helpful and deliver lower-cost, common access to many individuals. Recently, computer-aided diagnosis (CAD) approaches have been effectively employed to detect skin cancers in dermatoscopic imaging. The CAD-based techniques will be beneficial for helping professionals detect and classify skin lesions. This paper presents an Advanced Skin Lesion Classification using Block-Scrambling-Based Encryption with a Fusion of Transfer Learning Models and a Hippopotamus Optimization (SLCBSBE-FTLHO) model. The main aim of the SLCBSBE-FTLHO model relies on automating the diagnostic procedures of skin lesions using optimal DL approaches. At first, the block-scrambling-based encryption (BSBE) technique is utilized in the image encryption pre-processing stage, and then the decryption process is performed. The feature extraction process employs the fusion of MobileNetV2, GoogLeNet, and AlexNet techniques. Furthermore, the conditional variational autoencoder (CVAE) method is implemented for skin lesion classification. To optimize the CVAE model performance, the hippopotamus optimization (HO) model is utilized for hyperparameter tuning to ensure that the optimum hyperparameters are chosen for enhanced accuracy. To exhibit the improved performance of the SLCBSBE-FTLHO approach, a comprehensive experimental analysis is conducted under the skin cancer ISIC dataset. The comparative study of the SLCBSBE-FTLHO approach portrayed a superior accuracy value of 99.48% over existing models.https://doi.org/10.1038/s41598-025-04931-3Skin lesion classificationBlock-Scrambling-Based encryptionFusion of transfer learningHippopotamus optimization |
| spellingShingle | Ghada Moh. Samir Elhessewi Mohammed Yahya Alzahrani Mohammad Alamgeer Abdulbasit A. Darem Da’ad Albalawneh Mohammed Alqahtani Mutasim Al Sadig Sultan Alanazi Design of Block-Scrambling-Based privacy protection mechanism in healthcare using fusion of transfer learning models with Hippopotamus optimization algorithm Scientific Reports Skin lesion classification Block-Scrambling-Based encryption Fusion of transfer learning Hippopotamus optimization |
| title | Design of Block-Scrambling-Based privacy protection mechanism in healthcare using fusion of transfer learning models with Hippopotamus optimization algorithm |
| title_full | Design of Block-Scrambling-Based privacy protection mechanism in healthcare using fusion of transfer learning models with Hippopotamus optimization algorithm |
| title_fullStr | Design of Block-Scrambling-Based privacy protection mechanism in healthcare using fusion of transfer learning models with Hippopotamus optimization algorithm |
| title_full_unstemmed | Design of Block-Scrambling-Based privacy protection mechanism in healthcare using fusion of transfer learning models with Hippopotamus optimization algorithm |
| title_short | Design of Block-Scrambling-Based privacy protection mechanism in healthcare using fusion of transfer learning models with Hippopotamus optimization algorithm |
| title_sort | design of block scrambling based privacy protection mechanism in healthcare using fusion of transfer learning models with hippopotamus optimization algorithm |
| topic | Skin lesion classification Block-Scrambling-Based encryption Fusion of transfer learning Hippopotamus optimization |
| url | https://doi.org/10.1038/s41598-025-04931-3 |
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