Digital transformation in healthcare using eagle perching optimizer with deep learning model

Abstract

AbstractThe COVID‐19 epidemic accelerated the digital change of several services, including healthcare, and increased access to telemedicine. As a result, an increasing number of web tools were introduced to meet patient needs. A safe database can be created in the healthcare industry as a result of digital transformation. This database can be used to protect, store, and share private patient data with healthcare workers, labs, and medical specialists. Designing efficient decision‐making tools for COVID‐19 diagnostics is now possible thanks to recent developments in information technology and deep learning (DL) models. In this paper, a novel method for diagnosing COVID‐19 using deep learning‐enhanced eagle perching optimizer (DTH‐EPODL) model is presented. With the help of the IoT and the presented DTH‐EPODL model, patient information can be gathered and analysed for illness detection. The DTH‐EPODL model uses the Gaussian filtering (GF) method to remove noise in the initial step. Additionally, MixNet, a deep convolutional neural network‐based method, is used for feature extraction. Using the deep autoencoder (DAE) algorithm, COVID‐19 detection and categorization are accomplished. Finally, the DAE approach's associated hyperparameters can be best adjusted using the EPO method, which enhances categorization results. Benchmark chest x‐ray datasets can be used to evaluate the experimental validity of the DTH‐EPODL method. The experimental results showed that the DTH‐EPODL technique outperformed more modern methods.