Domain Specific Deep Neural Network Model for Classification of Abnormalities on Chest Radiographs

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Joy Nkechinyere Olawuyi
Joy Nkechinyere Olawuyi
2
Afolabi B.Samuel
Afolabi B.Samuel

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GJCST Volume 23 Issue D1

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The objective of our study was to evaluate, in a population of Togolese People Living With HIV(PLWHIV), the agreement between three scores derived from the general population namely the Framingham score, the Systematic Coronary Risk Evaluation (SCORE), the evaluation of the cardiovascular risk (CVR) according to the World Health Organization.
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This study collected, pre-processed dataset of chest radiographs, formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to develop a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture for making decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as parameter.

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No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

Joy Nkechinyere Olawuyi. 2026. \u201cDomain Specific Deep Neural Network Model for Classification of Abnormalities on Chest Radiographs\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 23 (GJCST Volume 23 Issue D1): .

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Alt: Academic research paper on deep neural networks for classifying radiographic abnormalities.
Issue Cover
GJCST Volume 23 Issue D1
Pg. 45- 53
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Classification
GJCST-D Classification: DDC Code: 006.32 LCC Code: QA76.87
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v1.2

Issue date

April 10, 2023

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English

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Domain Specific Deep Neural Network Model for Classification of Abnormalities on Chest Radiographs

Joy Nkechinyere Olawuyi
Joy Nkechinyere Olawuyi
Afolabi B.Samuel
Afolabi B.Samuel

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