COVID-19 detection using federated machine learning

Abstract

The current COVID-19 pandemic threatens human life, health, and productivity. AI plays an essential role in COVID-19 case classification as we can apply machine learning models on COVID-19 case data to predict infectious cases and recovery rates using chest x-ray. Accessing patient’s private data violates patient privacy and traditional machine learning model requires accessing or transferring whole data to train the model. In recent years, there has been increasing interest in federated machine learning, as it provides an effective solution for data privacy, centralized computation, and high computation power. In this paper, we studied the efficacy of federated learning versus traditional learning by developing two machine learning models (a federated learning model and a traditional machine learning model)using Keras and TensorFlow federated, we used a descriptive dataset and chest x-ray (CXR) images from COVID-19 patients. During the model training stage, we tried to identify which factors affect model prediction accuracy and loss like activation function, model optimizer, learning rate, number of rounds, and data Size, we kept recording and plotting the model loss and prediction accuracy per each training round, to identify which factors affect the model performance, and we found that softmax activation function and SGD optimizer give better prediction accuracy and loss, changing the number of rounds and learning rate has slightly effect on model prediction accuracy and prediction loss but increasing the data size did not have any effect on model prediction accuracy and prediction loss. finally, we build a comparison between the proposed models’ loss, accuracy, and performance speed, the results demonstrate that the federated machine learning model has a better prediction accuracy and loss but higher performance time than the traditional machine learning model.

Trends and hot topics in radiology, nuclear medicine and medical imaging from 2011–2021: a bibliometric analysis of highly cited papers

“…In 2017, Golden reported that AI can quickly read photos to diagnose breast cancer with lymph mode metastases, greatly improving the speed of diagnosis [ 36 ]. AI also played an important role in detecting COVID-19 [ 37 – 39 ]. In the future, AI is bound to exert greater influence on the medical field.…”

A multi-class classification framework for disease screening and disease diagnosis of COVID-19 from chest X-ray images

“…Since using AI for efficient detection of COVID 19 from chest X-ray images or chest CT scans requires patient records from hospitals or test records from testing facilities, privacy becomes a major issue therefore Federated Machine Learning is a promising area to explore in this context. The authors of [ 2 ] studied the efficacy of federated learning versus traditional learning. In this paper the authors used a descriptive dataset and chest X-ray images.…”

Cognitive computing-based COVID-19 detection on Internet of things-enabled edge computing environment

“…Abdul Salam et al ( 2021 ) investigated the efficiency of FL vs. conventional learning by emerging two ML modules (FL and conventional ML) with TensorFlow and Keras federated. In the module training phase, they attempt to detect which factor affects module predictive loss and accuracy, such as model optimizer, activation function, data size, number of rounds, and rate of learning, they saved plotting and recording the module predictive loss and accuracy for every training round, to detect which factor affects the module efficiency, and they discovered softmax activation function and SGD optimizer to provide optimum predictive loss and accuracy; altering the numbers of rounds and learning rate has somewhat influence on module predictive loss and accuracy; however, rising the data size did not have any effects on module predictive loss and accuracy.…”