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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, 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) |
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| Paper Title: | ACCURACY OF UNCERTAINTY AWARE MODELS FOR COVID-19 FOR X-RAY IMAGE CLASSIFICATION USING SMALL SCALE DATASETS |
| Authors Name: | Aakanksha Singh |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_206961
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| Published Paper Id: | IJNRDTH00092 |
| Published In: | Volume 8 Issue 11, November-2023 |
| DOI: | |
| 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. |
| Keywords: | CNN, Uncertainty-Aware Model, UA-ConvNET, Bayesian Based CNN |
| Cite Article: | "ACCURACY OF UNCERTAINTY AWARE MODELS FOR COVID-19 FOR X-RAY IMAGE CLASSIFICATION USING SMALL SCALE DATASETS", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 11, page no.750-819, November-2023, Available :http://www.ijnrd.org/papers/IJNRDTH00092.pdf |
| Downloads: | 000118771 |
| 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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Published Paper ID:IJNRDTH00092 Registration ID: 206961 Published In: Volume 8 Issue 11, November-2023 DOI (Digital Object Identifier): Page No: 750-819 Country: Gurgaon, Haryana, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRDTH00092 Published Paper PDF: https://www.ijnrd.org/papers/IJNRDTH00092 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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