A Smart Healthcare System Using Consumer Electronics and Federated Learning to Automatically Diagnose Diabetic Foot Ulcers.

Sujit Kumar Das; Nageswara Rao Moparthi; Suyel Namasudra; Rubén González Crespo; David Taniar

doi:10.9781/ijimai.2024.10.04

Authors

Sujit Kumar Das Siksha ‘O’ Anusandhan Deemed to be University.
Nageswara Rao Moparthi Amrita School of Computing.
Suyel Namasudra Department of Computer Science and Engineering, National Institute of Technology.
Rubén González Crespo Universidad Internacional de La Rioja.
David Taniar Faculty of Information Technology, Monash University.

DOI:

https://doi.org/10.9781/ijimai.2024.10.04

Keywords:

Data Augmentation, Data Confidentiality, Disease Diagnosis, Collaborative Learning, Convolutional Neural Network

Abstract

Privacy breaches on sensitive and widely distributed health data in consumer electronics (CE) demand novel strategies to protect privacy with correctness and proper operation maintenance. This work presents a scalable Federated Learning (FL) framework-based smart healthcare approach. Remote medical facilities frequently struggle with imbalanced datasets, including intermittent client connections to the FL global server. The proposed approach handled intermittent clients with diabetic foot ulcers (DFU) images. A data augmentation approach proposes to handle class imbalance problems during local model training. Also, a novel Convolutional Neural Network (CNN) architecture, ResKNet (K=4), is designed for client-side model training. The ResKNet is a sequence of distinctive residual blocks with 2D convolution, batch normalization, LeakyReLU activation, and skip connections (convolutional and identity). The proposed approach is evaluated for various client counts (5,10,15, and 20) and multiple test dataset sizes. The proposed framework can leverage consumer electronic devices and ensure secure data sharing among multiple sources. The potential of integrating the proposed approach with smartphones and wearable devices to provide highly secure data transmission is very high. The approach also helps medical institutions collaborate and develop a robust patient diagnostic model.

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References

A. K. Mishra, P. Roy, S. Bandyopadhyay, S. K. Das, “Feature fusion based machine learning pipeline to improve breast cancer prediction,” Multimedia Tools and Applications, vol. 81, no. 26, pp. 37627–37655, 2022, doi: 10.1007/s11042-022-13498-4.

S. Saminu, G. Xu, S. Zhang, I. Ab El Kader, H. A. Aliyu, A. H. Jabire, Y. K. Ahmed, M. J. Adamu, “Applications of artificial intelligence in automatic detection of epileptic seizures using eeg signals: A review,” in Artificial Intelligence and Applications, vol. 1, 2023, pp. 11–25.

S. Jiang, Y. Gu, E. Kumar, “Magnetic resonance imaging (mri) brain tumor image classification based on five machine learning algorithms,” Cloud Computing and Data Science, vol. 4, pp. 122–133, 2023, doi: 10.37256/ccds.4220232740.

R. J. Robinson, “Insights on cloud security management,” Cloud Computing and Data Science, vol. 4, pp. 212–222, 2023, doi: 10.37256/ccds.4220233292.

J. Purohit, R. Dave, “Leveraging deep learning techniques to obtain efficacious segmentation results,” Archives of Advanced Engineering Science, vol. 1, no. 1, pp. 11–26, 2023, doi: 10.47852/bonviewAAES32021220.

P. Voigt, A. Von dem Bussche, “The eu general data protection regulation (gdpr),” A Practical Guide, 1st Ed., Cham: Springer International Publishing, vol. 10, no. 3152676, pp. 10–5555, 2017, doi: 10.1007/978-3-319-57959-7.

S. Namasudra, P. Roy, “Size based access control model in cloud computing,” in Proc. of the International Conference on Electrical, Electronics, Signals, Communication and Optimization, 2015, pp. 1–4.

A.-R. Al-Ali, I. A. Zualkernan, M. Rashid, R. Gupta, M. AliKarar, “A smart home energy management system using iot and big data analytics approach,” IEEE Transactions on Consumer Electronics, vol. 63, no. 4, pp. 426–434, 2017, doi: 10.1109/TCE.2017.015014.

S. H. Requena, J. M. G. Nieto, A. Popov, I. N. Delgado, “Human activity recognition from sensorised patient’s data in healthcare: A streaming deep learning-based approach,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 8, no. 1, pp. 23–37, 2023, doi: https://hdl.handle.net/10630/27669.

Y. He, X. Jin, Q. Jiang, Z. Cheng, P. Wang, W. Zhou, “Lkat-gan: A gan for thermal infrared image colorization based on large kernel and attentionunet-transformer,” IEEE Transactions on Consumer Electronics, vol. 69, no. 3, pp. 478 – 489, 2023, doi: 10.1109/TCE.2023.3280165.

F. Ullah, G. Srivastava, H. Xiao, S. Ullah, J. C.- Lin, Y. Zhao, “A scalable federated learning approach for collaborative smart healthcare systems with intermittent clients using medical imaging,” IEEE Journal of Biomedical and Health Informatics, 2023, doi: 10.1109/JBHI.2023.3282955.

Z. Zhang, Y. Li, Y. Gong, Y. Yang, S. Ma, X. Guo, S. Ercisli, “Dataset and baselines for IID and OOD image classification considering data quality and evolving environments,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 8, no. 1, pp. 6-12, 2023, doi: 10.9781/ijimai.2023.01.007.

H. Guan, P.-T. Yap, A. Bozoki, M. Liu, “Federated learning for medical image analysis: A survey,” Pattern Recognition, p. 110424, 2024, doi: 10.1016/j.patcog.2024.110424.

V. Stephanie, I. Khalil, M. Atiquzzaman, X. Yi, “Trustworthy privacy-preserving hierarchical ensemble and federated learning in healthcare 4.0 with blockchain,” IEEE Transactions on Industrial Informatics, vol. 19, pp. 7936 – 7945, 2022, doi: 10.1109/TII.2022.3214998.

S. K. Das, S. Namasudra, A. K. Sangaiah, “Hcnnet: hybrid convolution neural network for automatic identification of ischaemia in diabetic foot ulcer wounds,” Multimedia Systems, vol. 30, no. 1, p. 36, 2024, doi: 10.1007/s00530-023-01241-4.

V. Filipe, P. Teixeira, A. Teixeira, “Automatic classification of foot thermograms using machine learning techniques,” Algorithms, vol. 15, no. 7, p. 236, 2022, doi: 10.3390/a15070236.

M. Goyal, N. D. Reeves, A. K. Davison, S. Rajbhandari, J. Spragg, M. H. Yap, “Dfunet: Convolutional neural networks for diabetic foot ulcer classification,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 4, no. 5, pp. 728–739, 2018, doi: 10.1109/TETCI.2018.2866254.

L. Alzubaidi, M. A. Fadhel, S. R. Oleiwi, O. Al- Shamma, J. Zhang, “Dfu_qutnet: diabetic foot ulcer classification using novel deep convolutional neural network,” Multimedia Tools and Applications, vol. 79, no. 21, pp. 15655–15677, 2020, doi: 10.1007/s11042-019- 07820-w.

F. Veredas, H. Mesa, L. Morente, “Binary tissue classification on wound images with neural networks and bayesian classifiers,” IEEE transactions on medical imaging, vol. 29, no. 2, pp. 410–427, 2009, doi: 10.1109/TMI.2009.2033595.

G. Scebba, J. Zhang, S. Catanzaro, C. Mihai, O. Distler, M. Berli, W. Karlen, “Detect-and-segment: a deep learning approach to automate wound image segmentation,” Informatics in Medicine Unlocked, vol. 29, p. 100884, 2022, doi: 10.1016/j.imu.2022.100884.

L. Wang, P. C. Pedersen, E. Agu, D. M. Strong, B. Tulu, “Area determination of diabetic foot ulcer images using a cascaded two-stage svm-based classification,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 9, pp. 2098–2109, 2016, doi: 10.1109/TBME.2016.2632522.

N. Ohura, R. Mitsuno, M. Sakisaka, Y. Terabe, Y. Morishige, A. Uchiyama, T. Okoshi, I. Shinji, A. Takushima, “Convolutional neural networks for wound detection: the role of artificial intelligence in wound care,” Journal of wound care, vol. 28, no. Sup10, pp. S13–S24, 2019, doi: 10.12968/jowc.2019.28.Sup10.S13.

M. Goyal, N. D. Reeves, S. Rajbhandari, N. Ahmad, C. Wang, M. H. Yap, “Recognition of ischaemia and infection in diabetic foot ulcers: Dataset and techniques,” Computers in Biology and Medicine, vol. 117, p. 103616, 2020, doi: 10.1016/j.compbiomed.2020.103616.

V. Rajinikanth, S. Kadry, P. Moreno-Ger, “Resnet18 supported inspection of tuberculosis in chest radiographs with integrated deep, lbp, and dwt features,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 8, no. 2, pp. 38–46, 2023, doi: 10.9781/ijimai.2023.05.004.

A. Gupta, S. Namasudra, “A novel technique for accelerating live migration in cloud computing,” Automated Software Engineering, vol. 29, no. 1, p. 34, 2022, doi: 10.1007/s10515-022-00332-2.

V. Singh, D. Jain, “A hybrid parallel classification model for the diagnosis of chronic kidney disease,” vol. 8, 2023, doi: 10.9781/ijimai.2021.10.008.

J.-H. Ahn, Y. Ma, S. Park, C. You, “Federated active learning (f-al): an efficient annotation strategy for federated learning,” IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3376746.

D. Li, W. Xie, Z. Wang, Y. Lu, Y. Li, L. Fang, “Feddiff: Diffusion model driven federated learning for multi-modal and multi-clients,” IEEE Transactions on Circuits and Systems for Video Technology, 2024, doi: 10.1109/TCSVT.2024.3407131.

S. A. Rieyan, M. R. K. News, A. M. Rahman, S. A. Khan, S. T. J. Zaarif, M. G. R. Alam, M. M. Hassan, M. Ianni, G. Fortino, “An advanced data fabric architecture leveraging homomorphic encryption and federated learning,” Information Fusion, vol. 102, p. 102004, 2024, doi: 10.1016/j.inffus.2023.102004.

S. Chen, L. Li, G. Wang, M. Pang, C. Shen, “Federated learning with heterogeneous quantization bit allocation and aggregation for internet of things,” IEEE Internet of Things Journal, vol. 11, pp. 3132–3143, 2023, doi: 10.1109/JIOT.2023.3296493.

M. D. Fathima, S. J. Samuel, S. Raja, “HDDSS: An Enhanced Heart Disease Decision Support System Using RFE-ABGNB Algorithm,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 8, no. 2, pp. 29-23, 2023, doi: 10.9781/ijimai.2021.10.003.

H. Elayan, M. Aloqaily, M. Guizani, “Sustainability of healthcare data analysis iot-based systems using deep federated learning,” IEEE Internet of Things Journal, vol. 9, no. 10, pp. 7338–7346, 2021, doi: 10.1109/JIOT.2021.3103635.

W. Sun, S. Lei, L. Wang, Z. Liu, Y. Zhang, “Adaptive federated learning and digital twin for industrial internet of things,” IEEE Transactions on Industrial Informatics, vol. 17, no. 8, pp. 5605–5614, 2020, doi: 10.1109/TII.2020.3034674.

Z. Li, X. Xu, X. Cao, W. Liu, Y. Zhang, D. Chen, H. Dai, “Integrated cnn and federated learning for covid-19 detection on chest x-ray images,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 21, pp. 835 – 845, 2022, doi: 10.1109/TCBB.2022.3184319.

M. A. Rahman, M. S. Hossain, M. S. Islam, N. A. Alrajeh, G. Muhammad, “Secure and provenance enhanced internet of health things framework: A blockchain managed federated learning approach,” IEEE Access, vol. 8, pp. 205071–205087, 2020, doi: 10.1109/ACCESS.2020.3037474.

T. K. Dang, X. Lan, J. Weng, M. Feng, “Federated learning for electronic health records,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 13, no. 5, pp. 1–17, 2022, doi: 10.1145/3514500.

P. Baheti, M. Sikka, K. Arya, R. Rajesh, “Federated learning on distributed medical records for detection of lung nodules.,” in VISIGRAPP (4: VISAPP), 2020, pp. 445–451.

L. Huang, A. L. Shea, H. Qian, A. Masurkar, H. Deng, D. Liu, “Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records,” Journal of biomedical informatics, vol. 99, p. 103291, 2019, doi: 10.1016/j.jbi.2019.103291.

J. Lee, J. Sun, F. Wang, S. Wang, C.-H. Jun, X. Jiang, et al., “Privacy-preserving patient similarity learning in a federated environment: development and analysis,” JMIR medical informatics, vol. 6, no. 2, p. e7744, 2018, doi: 10.2196/medinform.7744.