Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8650
Title: A Communication Efficient Federated Learning Approach to Multi Chest Diseases Classification
Authors: Çetinkaya, Alper Emin
Akin M.
Sagiroglu S.
Keywords: Chest diseases classification
Communication efficiency
Covid-19
Deep learning
Federated learning
Deep learning
Laws and legislation
Medical imaging
Chest disease classification
Chest X-ray image
Communication efficiency
Covid-19
Deep learning
Disease classification
Disease detection
Federated learning
Learning approach
Medical image analysis
Diagnosis
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Cetinkaya, 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.
Abstract: Recently, 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 IEEE
Description: 6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- -- 176826
URI: https://doi.org/10.1109/UBMK52708.2021.9558913
https://hdl.handle.net/20.500.11851/8650
ISBN: 9781665429085
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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