Paper Title

ACCURACY OF UNCERTAINTY AWARE MODELS FOR COVID-19 FOR X-RAY IMAGE CLASSIFICATION USING SMALL SCALE DATASETS

Article Identifiers

Registration ID: IJNRD_206961

Published ID: IJNRDTH00092

DOI: Click Here to Get

Authors

Aakanksha Singh

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.

How To Cite

"ACCURACY OF UNCERTAINTY AWARE MODELS FOR COVID-19 FOR X-RAY IMAGE CLASSIFICATION USING SMALL SCALE DATASETS", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 11, page no.750-819, November-2023, Available :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: 000121173

Research Area: Science & Technology

Country: Gurgaon, Haryana, India

Published Paper PDF: https://ijnrd.org/papers/IJNRDTH00092.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRDTH00092

About Publisher

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

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Call For Paper - Volume 10 | Issue 8 | August 2025

IJNRD is Scholarly open access journals, Peer-reviewed, and Refereed Journals, High Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool), Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) with Open-Access Publications.

INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

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