Abstract
To advance and/or ease computer aided diagnosis (CAD) system, chest X-ray (CXR) image view information is required. In other words, separating CXR image view: frontal and lateral can be considered as a crucial step to effective subsequent processes, since the techniques that work for frontal CXRs may not equally work for lateral ones. With this motivation, in this paper, we present a novel machine learning technique to classify frontal and lateral CXR images, where we introduce a force histogram to extract features and apply three different state-of-the-art classifiers: support vector machine (SVM), random forest (RF) and multi-layer perceptron (MLP). We validated our fully automatic technique on a set of 8100 images hosted by National Library of Medicine (NLM), National Institutes of Health (NIH), and achieved an accuracy close to 100%.
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Santosh, K.C., Wendling, L. (2017). Automated Chest X-ray Image View Classification using Force Histogram. In: Santosh, K., Hangarge, M., Bevilacqua, V., Negi, A. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2016. Communications in Computer and Information Science, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-4859-3_30
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DOI: https://doi.org/10.1007/978-981-10-4859-3_30
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