HDSNE a New Unsupervised Multiple Image Database Fusion Learning Algorithm with Flexible and Crispy Production of One Database: A Proof Case Study of Lung Infection Diagnose In Chest X-ray Images

Published: 14 November 2022| Version 1 | DOI: 10.17632/nttrfkg644.1
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Description

HDSNE is a New Unsupervised Multiple Image Database Fusion using Learning Algorithm to Diagnose Covid-19 by Chest X-ray Images. We proposed a global data aggregation scale model with six image databases selected from specific global resources. The Hash MD5 algorithm returns a unique hash value for each image, making it appropriate for duplication removal. The hash MD5 and T-SNE algorithms are applied recursively, producing a balanced and uniform database containing equal samples per category: normal, pneumonia, and COVID-19. The dataset is then cleaned by removing non-posteroanterior (PA) view and CT images that affect the model training process. These duplicates and out-of-scope images are removed during the curation process and a refined dataset is available for download. The final version of the aggregated dataset is 441 frontal X-ray images per class.

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Steps to reproduce

The Hash MD5 algorithm returns a unique hash value for each image per class, making it appropriate for duplication removal. Both the hash MD5 and T-SNE algorithms are applied recursively, producing a balanced and uniform database that contains equal samples per category: normal, pneumonia, and COVID-19.

Institutions

Sohag University Faculty of Science, Sohag University

Categories

Data Aggregation, X-Ray Imaging, Coronavirus, Digital Radiography, Convolutional Neural Network, Severe Acute Respiratory Syndrome Coronavirus 2, COVID-19, Transfer Learning, COVID-19 Diagnostics

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