TY - JOUR
KW - Data Augmentation
KW - Data Confidentiality
KW - Disease Diagnosis
KW - Collaborative Learning
KW - Convolutional Neural Network
AU - Sujit Kumar Das
AU - Nageswara Rao Moparth
AU - Suyel Namasudra
AU - Rubén González-Crespo
AU - David Taniar
AB - 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.
IS - In press
M1 - In press
N2 - 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.
PY - 9998
SE - 1
SP - 1
EP - 13
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - A Smart Healthcare System Using Consumer Electronics and Federated Learning to Automatically Diagnose Diabetic Foot Ulcers
UR - https://www.ijimai.org/journal/bibcite/reference/3502
VL - In press
SN - 1989-1660
ER -