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Open AccessArticle

Automatic Detection of Pulmonary Nodules using Three-dimensional Chain Coding and Optimized Random Forest

1
Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
2
School of Information and Telecommunication Engineering, Tokai University, Tokyo 108-8619, Japan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(7), 2346; https://doi.org/10.3390/app10072346
Received: 7 February 2020 / Revised: 24 March 2020 / Accepted: 25 March 2020 / Published: 29 March 2020
(This article belongs to the Section Computing and Artificial Intelligence)
The detection of pulmonary nodules on computed tomography scans provides a clue for the early diagnosis of lung cancer. Manual detection mandates a heavy radiological workload as it identifies nodules slice-by-slice. This paper presents a fully automated nodule detection with three significant contributions. First, an automated seeded region growing is designed to segment the lung regions from the tomography scans. Second, a three-dimensional chain code algorithm is implemented to refine the border of the segmented lungs. Lastly, nodules inside the lungs are detected using an optimized random forest classifier. The experiments for our proposed detection are conducted using 888 scans from a public dataset, and achieves a favorable result of 93.11% accuracy, 94.86% sensitivity, and 91.37% specificity, with only 0.0863 false positives per exam. View Full-Text
Keywords: automated seeded region growing; 3D chain code; firefly; lung cancer; pulmonary nodule; random forest automated seeded region growing; 3D chain code; firefly; lung cancer; pulmonary nodule; random forest
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Paing, M.P.; Hamamoto, K.; Tungjitkusolmun, S.; Visitsattapongse, S.; Pintavirooj, C. Automatic Detection of Pulmonary Nodules using Three-dimensional Chain Coding and Optimized Random Forest. Appl. Sci. 2020, 10, 2346.

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