An optimized EBRSA-Bi LSTM model for highly undersampled rapid CT image reconstruction

Sarvari, A.V.P.; Sridevi, K.

doi:10.1016/j.bspc.2023.104637

Cited by 2 publications

(1 citation statement)

References 39 publications

(47 reference statements)

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“…Figure 12 compares the validation loss of six deep learning models with our model. The validation loss curves were taken from six research papers, namely VGG-11 CNN, WideResNet-50-2 CNN, Inception v3 CNN, Harvard Dataverse: WideResNet-50-2 CNN [74], Bi-LSTM [75], and CNN-HOG [76], and redrawn to be compared with the results of our work. From the loss curves presented in Figure 12, it can be observed that the mean loss value for our CNN model is 0.068.…”

Section: Comparison Of Resultsmentioning

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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Section: Comparison Of Resultsmentioning

confidence: 99%