{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T18:28:56Z","timestamp":1781893736230,"version":"3.54.5"},"reference-count":86,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,8]],"date-time":"2022-01-08T00:00:00Z","timestamp":1641600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019 medical records have been shared using various wireless technologies. The wireless transmitted data are prone to attacks, resulting in the misuse of patients\u2019 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.<\/jats:p>","DOI":"10.3390\/s22020461","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:08:26Z","timestamp":1641769706000},"page":"461","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4228-385X","authenticated-orcid":false,"given":"Mujeeb Ur","family":"Rehman","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Riphah International University, Islamabad 46000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arslan","family":"Shafique","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Riphah International University, Islamabad 46000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kashif Hesham","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, RMIT University, Melbourne 3000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sohail","family":"Khalid","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Riphah International University, Islamabad 46000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah Alhumaidi","family":"Alotaibi","sequence":"additional","affiliation":[{"name":"Department of Science and Technology, College of Ranyah, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6674-7890","authenticated-orcid":false,"given":"Turke","family":"Althobaiti","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Science, Northern Border University, Arar 91431, Saudi Arabia"},{"name":"Remote Sensing Unit, Northern Border University, Arar 91431, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naeem","family":"Ramzan","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisely PA1 2BE, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6289-8248","authenticated-orcid":false,"given":"Jawad","family":"Ahmad","sequence":"additional","affiliation":[{"name":"School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2052-1121","authenticated-orcid":false,"given":"Syed Aziz","family":"Shah","sequence":"additional","affiliation":[{"name":"Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7097-9969","authenticated-orcid":false,"given":"Qammer H.","family":"Abbasi","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Priya, S., and Santhi, B. 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