Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model
This article presents non-invasive sensing-based diagnoses of pneumonia disease, exploiting a deep learning model to make the technique non-invasive coupled with security preservation. Sensing and securing healthcare and medical images such as X-rays that can be used to diagnose viral diseases such...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/2/461 |
| _version_ | 1797490515900366848 |
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| author | Mujeeb Ur Rehman Arslan Shafique Kashif Hesham Khan Sohail Khalid Abdullah Alhumaidi Alotaibi Turke Althobaiti Naeem Ramzan Jawad Ahmad Syed Aziz Shah Qammer H. Abbasi |
| author_facet | Mujeeb Ur Rehman Arslan Shafique Kashif Hesham Khan Sohail Khalid Abdullah Alhumaidi Alotaibi Turke Althobaiti Naeem Ramzan Jawad Ahmad Syed Aziz Shah Qammer H. Abbasi |
| author_sort | Mujeeb Ur Rehman |
| collection | DOAJ |
| description | This article presents non-invasive sensing-based diagnoses of pneumonia disease, exploiting a deep learning model to make the technique non-invasive coupled with security preservation. Sensing and securing healthcare and medical images such as X-rays that can be used to diagnose viral diseases such as pneumonia is a challenging task for researchers. In the past few years, patients’ medical records have been shared using various wireless technologies. The wireless transmitted data are prone to attacks, resulting in the misuse of patients’ medical records. Therefore, it is important to secure medical data, which are in the form of images. The proposed work is divided into two sections: in the first section, primary data in the form of images are encrypted using the proposed technique based on chaos and convolution neural network. Furthermore, multiple chaotic maps are incorporated to create a random number generator, and the generated random sequence is used for pixel permutation and substitution. In the second part of the proposed work, a new technique for pneumonia diagnosis using deep learning, in which X-ray images are used as a dataset, is proposed. Several physiological features such as cough, fever, chest pain, flu, low energy, sweating, shaking, chills, shortness of breath, fatigue, loss of appetite, and headache and statistical features such as entropy, correlation, contrast dissimilarity, etc., are extracted from the X-ray images for the pneumonia diagnosis. Moreover, machine learning algorithms such as support vector machines, decision trees, random forests, and naive Bayes are also implemented for the proposed model and compared with the proposed CNN-based model. Furthermore, to improve the CNN-based proposed model, transfer learning and fine tuning are also incorporated. It is found that CNN performs better than other machine learning algorithms as the accuracy of the proposed work when using naive Bayes and CNN is 89% and 97%, respectively, which is also greater than the average accuracy of the existing schemes, which is 90%. Further, K-fold analysis and voting techniques are also incorporated to improve the accuracy of the proposed model. Different metrics such as entropy, correlation, contrast, and energy are used to gauge the performance of the proposed encryption technology, while precision, recall, F1 score, and support are used to evaluate the effectiveness of the proposed machine learning-based model for pneumonia diagnosis. The entropy and correlation of the proposed work are 7.999 and 0.0001, respectively, which reflects that the proposed encryption algorithm offers a higher security of the digital data. Moreover, a detailed comparison with the existing work is also made and reveals that both the proposed models work better than the existing work. |
| first_indexed | 2024-03-10T00:34:05Z |
| format | Article |
| id | doaj.art-0c0b10aa18074161b88bf60ec692eddd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:34:05Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0c0b10aa18074161b88bf60ec692eddd2023-11-23T15:19:00ZengMDPI AGSensors1424-82202022-01-0122246110.3390/s22020461Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network ModelMujeeb Ur Rehman0Arslan Shafique1Kashif Hesham Khan2Sohail Khalid3Abdullah Alhumaidi Alotaibi4Turke Althobaiti5Naeem Ramzan6Jawad Ahmad7Syed Aziz Shah8Qammer H. Abbasi9Department of Electrical Engineering, Riphah International University, Islamabad 46000, PakistanDepartment of Electrical Engineering, Riphah International University, Islamabad 46000, PakistanDepartment of Computer Sciences, RMIT University, Melbourne 3000, AustraliaDepartment of Electrical Engineering, Riphah International University, Islamabad 46000, PakistanDepartment of Science and Technology, College of Ranyah, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Computer Science, Faculty of Science, Northern Border University, Arar 91431, Saudi ArabiaSchool of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisely PA1 2BE, UKSchool of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UKResearch Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UKJames Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UKThis article presents non-invasive sensing-based diagnoses of pneumonia disease, exploiting a deep learning model to make the technique non-invasive coupled with security preservation. Sensing and securing healthcare and medical images such as X-rays that can be used to diagnose viral diseases such as pneumonia is a challenging task for researchers. In the past few years, patients’ medical records have been shared using various wireless technologies. The wireless transmitted data are prone to attacks, resulting in the misuse of patients’ medical records. Therefore, it is important to secure medical data, which are in the form of images. The proposed work is divided into two sections: in the first section, primary data in the form of images are encrypted using the proposed technique based on chaos and convolution neural network. Furthermore, multiple chaotic maps are incorporated to create a random number generator, and the generated random sequence is used for pixel permutation and substitution. In the second part of the proposed work, a new technique for pneumonia diagnosis using deep learning, in which X-ray images are used as a dataset, is proposed. Several physiological features such as cough, fever, chest pain, flu, low energy, sweating, shaking, chills, shortness of breath, fatigue, loss of appetite, and headache and statistical features such as entropy, correlation, contrast dissimilarity, etc., are extracted from the X-ray images for the pneumonia diagnosis. Moreover, machine learning algorithms such as support vector machines, decision trees, random forests, and naive Bayes are also implemented for the proposed model and compared with the proposed CNN-based model. Furthermore, to improve the CNN-based proposed model, transfer learning and fine tuning are also incorporated. It is found that CNN performs better than other machine learning algorithms as the accuracy of the proposed work when using naive Bayes and CNN is 89% and 97%, respectively, which is also greater than the average accuracy of the existing schemes, which is 90%. Further, K-fold analysis and voting techniques are also incorporated to improve the accuracy of the proposed model. Different metrics such as entropy, correlation, contrast, and energy are used to gauge the performance of the proposed encryption technology, while precision, recall, F1 score, and support are used to evaluate the effectiveness of the proposed machine learning-based model for pneumonia diagnosis. The entropy and correlation of the proposed work are 7.999 and 0.0001, respectively, which reflects that the proposed encryption algorithm offers a higher security of the digital data. Moreover, a detailed comparison with the existing work is also made and reveals that both the proposed models work better than the existing work.https://www.mdpi.com/1424-8220/22/2/461encryptionsecuritydeep learningmachine learningchaos |
| spellingShingle | Mujeeb Ur Rehman Arslan Shafique Kashif Hesham Khan Sohail Khalid Abdullah Alhumaidi Alotaibi Turke Althobaiti Naeem Ramzan Jawad Ahmad Syed Aziz Shah Qammer H. Abbasi Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model Sensors encryption security deep learning machine learning chaos |
| title | Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model |
| title_full | Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model |
| title_fullStr | Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model |
| title_full_unstemmed | Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model |
| title_short | Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model |
| title_sort | novel privacy preserving non invasive sensing based diagnoses of pneumonia disease leveraging deep network model |
| topic | encryption security deep learning machine learning chaos |
| url | https://www.mdpi.com/1424-8220/22/2/461 |
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