{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/false-positive-reduction-in-lung-computed","title":"False Positive Reduction in Lung Computed Tomography Images using Convolutional Neural Networks","arxiv_id":"1811.01424","date":"2018-11-04","proceeding":null,"authors":["Gorkem Polat","Ugur Halici","Yesim Serinagaoglu Dogrusoz"],"abstract":"Recent studies have shown that lung cancer screening using annual low-dose\ncomputed tomography (CT) reduces lung cancer mortality by 20% compared to\ntraditional chest radiography. Therefore, CT lung screening has started to be\nused widely all across the world. However, analyzing these images is a serious\nburden for radiologists. In this study, we propose a novel and simple framework\nthat analyzes CT lung screenings using convolutional neural networks (CNNs) and\nreduces false positives. Our framework shows that even non-complex\narchitectures are very powerful to classify 3D nodule data when compared to\ntraditional methods. We also use different fusions in order to show their power\nand effect on the overall score. 3D CNNs are preferred over 2D CNNs because\ndata are in 3D, and 2D convolutional operations may result in information loss.\nMini-batch is used in order to overcome class-imbalance. Proposed framework has\nbeen validated according to the LUNA16 challenge evaluation and got score of\n0.786, which is the average sensitivity values at seven predefined false\npositive (FP) points.","url_abs":"http://arxiv.org/abs/1811.01424v1","url_pdf":"http://arxiv.org/pdf/1811.01424v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"false-positive-reduction-in-lung-computed","repo_url":"https://github.com/GarkP/LUNA16_Challange","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"false-positive-reduction-in-lung-computed","repo_url":"https://github.com/GorkemP/LUNA16_Challange","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}