An integrated framework for COVID‐19 classification based on classical and quantum transfer learning from a chest radiograph. (29th June 2021)

• Record Type: Journal Article
• Authors: Umer, Muhammad Junaid; Amin, Javeria; Sharif, Muhammad; Anjum, Muhammad Almas; Azam, Faisal; Shah, Jamal Hussain
• Other Names: Wright Steven A. guestEditor.; Solak Serdar guestEditor.; Kilimci Zeynep Hilal guestEditor.; Eken Süleyman guestEditor.; Fernandes Steven guestEditor.; Zhang Yu‐Dong guestEditor.; Tavares João Manuel R.S. guestEditor.
• Is Part Of: Concurrency and computation. Volume 34:Number 20(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-29
• Subjects: classification -- COVID‐19 -- deep features -- feature selection -- fusion -- quantum -- SVM; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6434
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: COVID‐19 is a quickly spreading over 10 million persons globally. The overall number of infected patients worldwide is estimated to be around 133, 381, 413 people. Infection rate is being increased on daily basis. It has also caused a devastating effect on the world economy and public health. Early stage detection of this disease is mandatory to reduce the mortality rate. Artificial intelligence performs a vital role for COVID‐19 detection at an initial stage using chest radiographs. The proposed methods comprise of the two phases. Deep features (DFs) are derived from its last fully connected layers of pre‐trained models like AlexNet and MobileNet in phase‐I. Later these feature vectors are fused serially. Best features are selected through feature selection method of PCA and passed to the SVM and KNN for classification. In phase‐II, quantum transfer learning model is utilized, in which a pre‐trained ResNet‐18 model is applied for DF collection and then these features are supplied as an input to the 4‐qubit quantum circuit for model training with the tuned hyperparameters. The proposed technique is evaluated on two publicly available x‐ray imaging datasets. The proposed methodology achieved an accuracy index of 99.0% with three classes including corona virus‐positive images, normal images, and pneumonia radiographs. In comparison to other recently published work, the experimental findings show that the proposed approach outperforms it.