DeePNeu: Robust Detection of Pneumonia Symptoms using Faster R-CNN

  • I Wayan Ordiyasa Universitas Respati Yogyakarta
  • Akhyar Bintang Universitas Syiah Kuala

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 to eliminate the prediction of the bounding box that is less precise to increase accuracy

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Published
2022-10-16
How to Cite
ORDIYASA, I Wayan; BINTANG, Akhyar. DeePNeu: Robust Detection of Pneumonia Symptoms using Faster R-CNN. International Journal of Informatics and Computation, [S.l.], v. 4, n. 1, p. 21-32, oct. 2022. ISSN 2714-5263. Available at: <https://ijicom.respati.ac.id/index.php/ijicom/article/view/46>. Date accessed: 02 dec. 2022. doi: https://doi.org/10.35842/ijicom.v4i1.46.