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dc.contributor.authorÇetinkaya, Alper Emin-
dc.contributor.authorAkin M.-
dc.contributor.authorSagiroglu S.-
dc.identifier.citationCetinkaya, A. E., Akin, M., & Sagiroglu, S. (2021, September). A Communication Efficient Federated Learning Approach to Multi Chest Diseases Classification. In 2021 6th International Conference on Computer Science and Engineering (UBMK) (pp. 429-434). IEEE.en_US
dc.description6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- -- 176826en_US
dc.description.abstractRecently, deep learning (DL) has been playing a crucial role in aiding disease detection and started a vital role in medical image analysis. Due to its numerous advantages such as promising accuracy, ease of scalability, and fast diagnosis, DL has been a practical alternative to laboratory-based disease detection methods, especially for the Covid-19 detection. DL also has some disadvantages for privacy concerns, legal regulations, and hardware needs. To address this issue, we adopted the federated learning (FL) approach which is a novel data-private collaborative learning procedure that enables leveraging the big and diverse data securely without collecting the data into a central place. This paper introduces a CNN architecture for multi chest-diseases classification from chest X-ray (CXR) images. The proposed classification model has reached %93.34 accuracy on the task. A combination of various publicly available datasets also presented with 28833 CXR images, a mixture of Covid-19, viral or bacterial pneumonia, lung opacity, and normal cases. The proposed FL based model also achieves the tasks with %92.96 accuracy. Weight pruning and quantization were implemented to reduce the communication cost by 10x of FL which is usually the primary bottleneck in FL. The communication efficient federated training achieves the task with %92.44 accuracy with a negligible loss in the accuracy. Lastly, the results of central training, federated training and communication efficient federated training were given and compared with the experiments. It is expected that the proposed model might help to encourage organizations or researchers to develop their own models, to improve collaboration, to increase data utility, and to get more benefits from unused data by adopting the federated learning approach. © 2021 IEEEen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021en_US
dc.subjectChest diseases classificationen_US
dc.subjectCommunication efficiencyen_US
dc.subjectDeep learningen_US
dc.subjectFederated learningen_US
dc.subjectDeep learningen_US
dc.subjectLaws and legislationen_US
dc.subjectMedical imagingen_US
dc.subjectChest disease classificationen_US
dc.subjectChest X-ray imageen_US
dc.subjectCommunication efficiencyen_US
dc.subjectDeep learningen_US
dc.subjectDisease classificationen_US
dc.subjectDisease detectionen_US
dc.subjectFederated learningen_US
dc.subjectLearning approachen_US
dc.subjectMedical image analysisen_US
dc.titleA Communication Efficient Federated Learning Approach to Multi Chest Diseases Classificationen_US
dc.typeConference Objecten_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümüen_US
dc.departmentFaculties, Faculty of Engineering, Department of Electrical and Electronics Engineeringen_US
dc.institutionauthorÇetinkaya, Alper Emin-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - İdari Personel ve Öğrencien_US
item.fulltextNo Fulltext-
item.openairetypeConference Object-
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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