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DeePNeu: Robust Detection of Pneumonia Symptoms using Faster R-CNN
ISSN: ISSN - N268
Publisher: author
DeePNeu: Robust Detection of Pneumonia Symptoms using Faster R-CNN
Indexed in
Technology and Engineering
ARTICLE-FACTOR
1.3
Article Basics Score: 3
Article Transparency Score: 2
Article Operation Score: 2
Article Articles Score: 3
Article Accessibility Score: 2
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International Category Code (ICC):
ICC-1802
Publisher: International Journal Of Informatics And Computation (ijic..
eISSN
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ISSN - N268
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Abstract
Every year, more than 150 million people, primarily children under five, develop pneumonia. Various articles present various methods for detecting pneumonia. However, to accurately analyze chest X-ray images, radiologists need expertise field. The traditional techniques remain shortcomings, including the availability of experts, maintenance costs, and expensive tools. Thus, we present a new intelligence method to detect pneumonia images quickly and accurately using the Faster Region Convolutional Neural Network (Faster R-CNN) algorithm. To build our detection model, we collect data, process it first, train it with various parameters to get the best accuracy, and then test it with new data. Based on the experimental results, it was found that this model can accurately detect pneumonia x-ray images marked with bounding boxes. In this model, it is possible to predict the bounding box that is more than what it should be, so NMS is applied...
