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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2020,1,31]]},"abstract":"<jats:p>The early diagnosis of pulmonary cancer can significantly improve the survival rate of patients, where pulmonary nodules detection in computed tomography images plays an important role. In this article, we propose a novel pulmonary nodule detection system based on convolutional neural networks (CNN). Our system consists of two stages, pulmonary nodule candidate detection and false positive reduction. For candidate detection, we introduce Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) to Faster Region-based Convolutional Neural Network (Faster R-CNN) model. For false positive reduction, a three-dimensional convolutional neural network (3D-CNN) is employed to completely utilize the three-dimensional nature of CT images. In this network, Focal Loss is used to solve the class imbalance problem in this task. Experiments were conducted on LUNA16 dataset. The results show the preferable performance of the proposed system and the effectiveness of using ISODATA and Focal loss in pulmonary nodule detection is proved.<\/jats:p>","DOI":"10.1145\/3365445","type":"journal-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T07:01:36Z","timestamp":1588575696000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Pulmonary Nodule Detection Based on ISODATA-Improved Faster RCNN and 3D-CNN with Focal Loss"],"prefix":"10.1145","volume":"16","author":[{"given":"Chao","family":"Tong","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University and National Engineering Laboratory for Internet Medical System and Application, The First Affiliated Hospital of Zhengzhou University, Beijing, China"}]},{"given":"Baoyu","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University and National Engineering Laboratory for Internet Medical System and Application, The First Affiliated Hospital of Zhengzhou University, China"}]},{"given":"Mengze","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University and National Engineering Laboratory for Internet Medical System and Application, The First Affiliated Hospital of Zhengzhou University, China"}]},{"given":"Rongshan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University and National Engineering Laboratory for Internet Medical System and Application, The First Affiliated Hospital of Zhengzhou University, China"}]},{"given":"Arun Kumar","family":"Sangaiah","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8774-4568","authenticated-orcid":false,"given":"Zhigao","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}]},{"given":"Tao","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing, China"}]},{"given":"Chenyang","family":"Yue","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Capital Normal University, Beijing, China"}]},{"given":"Xinyi","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2020,4,17]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.06.015"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3528204"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21492"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","unstructured":"J. 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DOI:https:\/\/doi.org\/10.1007\/s11517-016-1582-x 10.1007\/s11517-016-1582-x Antonio Oseas de Carvalho Filho, Arist\u00f3fanes Corr\u00eaa Silva, Anselmo Cardoso de Paiva, Rodolfo Acatauass\u00fa Nunes, and Marcelo Gattass. 2017. 3D shape analysis to reduce false positives for lung nodule detection systems. Medical 8 Biological Engineering 8 Computing 55, 8 (1 Aug 2017), 1199--1213. DOI:https:\/\/doi.org\/10.1007\/s11517-016-1582-x"},{"key":"e_1_2_1_9_1","doi-asserted-by":"crossref","unstructured":"H. Jin Z. Li R. Tong and L. Lin. 2018. A deep 3D residual CNN for false positive reduction in pulmonary nodule detection. Medical Physics 45 5 (2018).  H. Jin Z. Li R. Tong and L. Lin. 2018. A deep 3D residual CNN for false positive reduction in pulmonary nodule detection. 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