{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T16:34:55Z","timestamp":1788971695800,"version":"build-2803163510"},"reference-count":30,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,3,30]],"date-time":"2024-03-30T00:00:00Z","timestamp":1711756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Bedfordshire","award":["IRAC PGR RESCH200"],"award-info":[{"award-number":["IRAC PGR RESCH200"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Malaria is one of the life-threatening diseases caused by the parasite known as Plasmodium falciparum, affecting the human red blood cells. Therefore, it is an important to have an effective computer-aided system in place for early detection and treatment. The visual heterogeneity of the malaria dataset is highly complex and dynamic, therefore higher number of images are needed to train the machine learning (ML) models effectively. However, hospitals as well as medical institutions do not share the medical image data for collaboration due to general data protection regulations (GDPR) and the data protection act (DPA). To overcome this collaborative challenge, our research utilised real-time medical image data in the framework of federated learning (FL). We have used state-of-the-art ML models that include the ResNet-50 and DenseNet in a federated learning framework. We have experimented both models in different settings on a malaria dataset constituting 27,560 publicly available images and our preliminary results showed that the DenseNet model performed better in accuracy (75%) in contrast to ResNet-50 (72%) while considering eight clients, while the trend was observed as common in four clients with the similar accuracy of 94%, and six clients showed that the DenseNet model performed quite well with the accuracy of 92%, while ResNet-50 achieved only 72%. The federated learning framework enhances the accuracy due to its decentralised nature, continuous learning, and effective communication among clients, as well as the efficient local adaptation. The use of federated learning architecture among the distinct clients for ensuring the data privacy and following GDPR is the contribution of this research work.<\/jats:p>","DOI":"10.3390\/bioengineering11040340","type":"journal-article","created":{"date-parts":[[2024,3,31]],"date-time":"2024-03-31T13:39:37Z","timestamp":1711892377000},"page":"340","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Privacy-Preserving Approach to Effectively Utilize Distributed Data for Malaria Image Detection"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1968-8576","authenticated-orcid":false,"given":"Amer","family":"Kareem","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0390-3657","authenticated-orcid":false,"given":"Haiming","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9980-9368","authenticated-orcid":false,"given":"Vladan","family":"Velisavljevic","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"629654","DOI":"10.1155\/2012\/629654","article-title":"Bayesian Assessment of Newborn Brain Maturity from Two-Channel Sleep Electroencephalograms","volume":"2012","author":"Jakaite","year":"2012","journal-title":"Comput. Math. Methods Med."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jakaite, L., Schetinin, V., Maple, C., and Schult, J. (2010, January 8\u201310). Bayesian Decision Trees for EEG Assessment of newborn brain maturity. Proceedings of the 2010 UK Workshop on Computational Intelligence (UKCI), Colchester, UK.","DOI":"10.1109\/UKCI.2010.5625584"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Balyan, A.K., Ahuja, S., Sharma, S.K., and Lilhore, U.K. (2022, January 26\u201327). Machine Learning-Based Intrusion Detection System For Healthcare Data. Proceedings of the 2022 IEEE VLSI Device Circuit and System (VLSI DCS), Kolkata, India.","DOI":"10.1109\/VLSIDCS53788.2022.9811465"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.cmpb.2013.05.015","article-title":"Bayesian Decision Trees for predicting survival of patients: A study on the US National Trauma Data Bank","volume":"111","author":"Schetinin","year":"2013","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.icte.2018.10.005","article-title":"Diabetes detection using deep learning algorithms","volume":"4","author":"Swapna","year":"2018","journal-title":"ICT Express"},{"key":"ref_6","unstructured":"WHO (2020). Responding to Community Spread of COVID-19. Reference WHO\/COVID-19\/Community_Transmission\/2020.1, World Health Organization."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Musleh, A.A.W.A., and Maghari, A.Y. (2020, January 16\u201317). COVID-19 Detection in X-ray Images using CNN Algorithm. Proceedings of the 2020 International Conference on Promising Electronic Technologies (ICPET), Jerusalem, Palestine.","DOI":"10.1109\/ICPET51420.2020.00010"},{"key":"ref_8","unstructured":"(2024, March 16). Data Protection Act 2018, Available online: https:\/\/www.legislation.gov.uk\/ukpga\/2018\/12\/part\/2\/chapter\/2\/enacted."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4635","DOI":"10.1109\/JBHI.2022.3185956","article-title":"Splitavg: A heterogeneity-aware federated deep learning method for medical imaging","volume":"26","author":"Zhang","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Techavipoo, U., Sinsuebphon, N., Prompalit, S., Thongvigitmanee, S., Narkbuakaew, W., Kiang-Ia, A., Srivongsa, T., Thajchayapong, P., and Chaumrattanakul, U. (2021). Image Quality Evaluation of a Digital Radiography System Made in Thailand. BioMed Res. Int., 2021.","DOI":"10.1155\/2021\/3102673"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ali, S., and Raut, S. (2023, January 5\u20136). Detection of Diabetic Retinopathy from fundus images using Resnet50. Proceedings of the 2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS), Nagpur, India.","DOI":"10.1109\/PCEMS58491.2023.10136073"},{"key":"ref_12","unstructured":"Sundari, S.M., Rao, M.D.S., Rani, M.S., Durga, K., and Kranthi, A. (2022, January 14\u201316). COVID-19 X-ray Image Detection using ResNet50 and VGG16 in Convolution Neural Network. Proceedings of the 2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), Gwalior, India."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1750064","DOI":"10.1142\/S0129065717500642","article-title":"Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification","volume":"28","author":"Schetinin","year":"2018","journal-title":"Int. J. Neural Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"42839","DOI":"10.1109\/ACCESS.2023.3270774","article-title":"An Improved Densenet Deep Neural Network Model for Tuberculosis Detection Using Chest X-Ray Images","volume":"11","author":"Huy","year":"2023","journal-title":"IEEE Access"},{"key":"ref_15","unstructured":"Anjugam, S., and Arul Leena Rose, P.J. (2023, January 22\u201323). Study of Deep Learning Approaches for Diagnosing COVID-19 Disease using Chest CT Images. Proceedings of the 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chowdhury, D., Unnikannan, A., Ghosh, A., Dutta, A., Deo, D.S., Saha, D., Bhowmick, M., Majumdar, M., Bhowmik, S., and De, S. (2023, January 10\u201311). Detection of SARS-CoV-2 from human chest CT images in Multi-Convolutional Neural Network\u2019s environment. Proceedings of the 2023 11th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON), Jaipur, India.","DOI":"10.1109\/IEMECON56962.2023.10092370"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Liu, Y., Wang, Q., and Toe, T.T. (2023, January 29\u201331). Detection of Pneumonia Based on ResNet Improved by Attention Mechanism. Proceedings of the 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA), Shenyang, China.","DOI":"10.1109\/ICPECA56706.2023.10076216"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"35","DOI":"10.18280\/isi.250105","article-title":"Classification of malaria cell images with deep learning architectures","volume":"25","author":"Yildirim","year":"2020","journal-title":"Ing\u00e9nierie Des Syst\u00e8mes D\u2019information"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"48292","DOI":"10.1109\/ACCESS.2023.3276810","article-title":"Knee Osteoarthritis Detection and Classification Using X-rays","volume":"11","author":"Tariq","year":"2023","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez-S\u00e1nchez, A., Tardy, M., Ballester, M.A.G., Mateus, D., and Piella, G. (2023). Memory-aware curriculum federated learning for breast cancer classification. Comput. Methods Programs Biomed., 229.","DOI":"10.1016\/j.cmpb.2022.107318"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"28628","DOI":"10.1109\/ACCESS.2023.3260027","article-title":"A Systematic Review on Federated Learning in Medical Image Analysis","volume":"11","author":"Sohan","year":"2023","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101992","DOI":"10.1016\/j.media.2021.101992","article-title":"Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China Italy Japan","volume":"70","author":"Yang","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/OJCS.2022.3206407","article-title":"Collaborative federated learning for healthcare: Multi-modal COVID-19 diagnosis at the edge","volume":"3","author":"Qayyum","year":"2022","journal-title":"IEEE Open J. Comput. Soc."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Salam, M.A., Taha, S., and Ramadan, M. (2021). COVID-19 detection using federated machine learning. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0252573"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1953","DOI":"10.1038\/s41598-022-05539-7","article-title":"Federated learning and differential privacy for medical image analysis","volume":"12","author":"Adnan","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Deng, Y., Ren, J., Tang, C., Lyu, F., Liu, Y., and Zhang, Y. (2023, January 17\u201320). A hierarchical knowledge transfer framework for heterogeneous federated learning. Proceedings of the IEEE INFOCOM 2023-IEEE Conference on Computer Communications, New York, NY, USA.","DOI":"10.1109\/INFOCOM53939.2023.10228954"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"114084","DOI":"10.1016\/j.dss.2023.114084","article-title":"A novel federated learning approach with knowledge transfer for credit scoring","volume":"177","author":"Wang","year":"2024","journal-title":"Decis. Support Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Albarqouni, S., Bakas, S., Bano, S., Cardoso, M.J., Khanal, B., Landman, B., Li, X., Qin, C., Rekik, I., and Rieke, N. (2022). Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health: Third MICCAI Workshop, DeCaF 2022, and Second MICCAI Workshop, FAIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, Proceedings, Springer Nature.","DOI":"10.1007\/978-3-031-18523-6"},{"key":"ref_29","unstructured":"(2024, January 07). Available online: https:\/\/www.kaggle.com\/datasets\/iarunava\/cell-images-for-detecting-malaria."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1080\/00031305.2022.2115552","article-title":"How Do We Perform a Paired t-Test When We Don\u2019t Know How to Pair?","volume":"77","author":"Grabchak","year":"2023","journal-title":"Am. Stat."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/4\/340\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:21:27Z","timestamp":1760106087000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/4\/340"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,30]]},"references-count":30,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["bioengineering11040340"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering11040340","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,30]]}}}