Paper Title
ACCURACY OF UNCERTAINTY AWARE MODELS FOR COVID-19 FOR X-RAY IMAGE CLASSIFICATION USING SMALL SCALE DATASETS
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Keywords
CNN, Uncertainty-Aware Model, UA-ConvNET, Bayesian Based CNN
Abstract
To better understand uncertainty, the report assess the precision of models that are sensitive to it and apply analytical expertise to chest X-ray images. confidence in the sickness. Our goal is to apply these methods to a dataset that is available to the general public and then track and compare the metrics, especially the model accuracy. The preprocessing of three different datasets which are available publicly. Training the datasets the following models which are pre- existing: were EfficientNet, UA-ConvNet, Bayesian optimization-based convolutional neural network (CNN), COVID-CXNet, Deep Channel Boosted STM-RENet, CVD-HNet. Evaluating the efficiency of models by different metrices which are, precision, recall, sensitivity, specificity, AUC and especially accuracy. Comparing the Accuracy of different models and finding out which model performs when same datasets are used for all models. The Testing Results incurred by EfficientNET-B3 model were as accuracy for DatasetA is approx. 66%, DatasetB is approx. 67% and DatasetC is approx. 67%. For UA-ConvNET model, the accuracy observed were 66% for DatasetA, 67% for DatasetB and 45% for DatasetC. The Bayesian based CNN model performed really well having accuracy as were 97% for DatasetA, 71% for DatasetB and 61% for DatasetC.
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How To Cite (APA)
Aakanksha Singh (November-2023). ACCURACY OF UNCERTAINTY AWARE MODELS FOR COVID-19 FOR X-RAY IMAGE CLASSIFICATION USING SMALL SCALE DATASETS. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(11), 750-819. https://ijnrd.org/papers/IJNRDTH00092.pdf
Issue
Volume 8 Issue 11, November-2023
Pages : 750-819
Other Publication Details
Paper Reg. ID: IJNRD_206961
Published Paper Id: IJNRDTH00092
Downloads: 000122017
Research Area: Science & Technology
Author Type: Indian Author
Country: Gurgaon, Haryana, India
Published Paper PDF: https://ijnrd.org/papers/IJNRDTH00092.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRDTH00092
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