COVID-19 Detection via Image Classification using Deep Learning on Chest X-Ray

Azeem, Mohammad Ayyaz; Khan, Muhammad Irfan; Khan, Suleiman A.

doi:10.1109/ee-rds53766.2021.9708588

Cited by 7 publications

(1 citation statement)

References 11 publications

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“…We suggest training process variables initially set to optimize the CNN model parameters to identify a specific control set for obtaining the best results. Before training beings, the model is provided with tabbed numerical data (as explained in Section 3.1), divided data subsets (as described in Section 3.3) [47,66], with the number of epochs at 1000 for training [67], and a learning rate of 0.001 [38,68,69]. The Adam optimizer [38,70] updates the parameters for all layers frequently, and the FC layer1 is configured with 512 nodes and FC layer2 with 64 nodes for detection [71].…”

Section: Experimental Hyperparametersmentioning

confidence: 99%

Deep learning‐based COVID‐19 diagnosis using CT scans with laboratory and physiological parameters

Sameer

Mutlag

Gharghan

2023

IET Image Processing

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The global economy has been dramatically impacted by COVID‐19, which has spread to be a pandemic. COVID‐19 virus affects the respiratory system, causing difficulty breathing in the patient. It is crucial to identify and treat infections as soon as possible. Traditional diagnostic reverse transcription‐polymerase chain reaction (RT‐PCR) methods require more time to find the infection. A high infection rate, slow laboratory analysis, and delayed test results caused the widespread and uncontrolled spread of the disease. This study aims to diagnose the COVID‐19 epidemic by leveraging a modified convolutional neural network (CNN) to quickly and safely predict the disease's appearance from computed tomography (CT) scan images and a laboratory and physiological parameters dataset. A dataset representing 500 patients was used to train, test, and validate the CNN model with results in detecting COVID‐19 having an accuracy, sensitivity, specificity, and F1‐score of 99.33%, 99.09%, 99.52%, and 99.24%, respectively. These experimental results suggest that our strategy performs better than previously published approaches.

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