Chest x-ray image classification using faster R-CNN / Taufik Rahmat, Azlan Ismail and Sharifah Aliman

Rahmat, Taufik and Ismail, Azlan and Aliman, Sharifah (2019) Chest x-ray image classification using faster R-CNN / Taufik Rahmat, Azlan Ismail and Sharifah Aliman. Malaysian Journal of Computing (MJoC), 4 (1). pp. 225-236. ISSN 2600-8238

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Chest x-ray image analysis is the common medical imaging exam needed to assess different pathologies. Having an automated solution for the analysis can contribute to minimizing the workloads, improve efficiency and reduce the potential of reading errors. Many methods have been proposed to address chest x-ray image classification and detection. However, the application of regional-based convolutional neural networks (CNN) is currently limited. Thus, we propose an approach to classify chest x-ray images into either one of two categories, pathological or normal based on Faster Regional-CNN model. This model utilizes Region Proposal Network (RPN) to generate region proposals and perform image classification. By applying this model, we can potentially achieve two key goals, high confidence in the classification and reducing the computation time. The results show the applied model achieved higher accuracy as compared to the medical representatives on the random chest x-ray images. The classification model is also reasonably effective in classifying between finding and normal chest x-ray image captured through a live webcam.


Item Type: Article
Email / ID Num.
Rahmat, Taufik
Ismail, Azlan
Aliman, Sharifah
Subjects: Q Science > QC Physics > Radiation physics (General) > X-rays
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Computer and Mathematical Sciences
Journal or Publication Title: Malaysian Journal of Computing (MJoC)
UiTM Journal Collections: UiTM Journal > Malaysian Journal of Computing (MJoC)
ISSN: 2600-8238
Volume: 4
Number: 1
Page Range: pp. 225-236
Keywords: Image Classification, Chest X-ray analysis, CNN, faster R-CNN, Region proposal network
Date: June 2019
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