{"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/computer-aided-detection-of-oral-lesions-on","title":"Computer Aided Detection of Oral Lesions on CT Images","arxiv_id":"1611.09769","date":"2016-11-29","proceeding":null,"authors":["Shaikat Galib","Fahima Islam","Muhammad Abir","Hyoung-Koo Lee"],"abstract":"Oral lesions are important findings on computed tomography (CT) images. In\nthis study, a fully automatic method to detect oral lesions in mandibular\nregion from dental CT images is proposed. Two methods were developed to\nrecognize two types of lesions namely (1) Close border (CB) lesions and (2)\nOpen border (OB) lesions, which cover most of the lesion types that can be\nfound on CT images. For the detection of CB lesions, fifteen features were\nextracted from each initial lesion candidates and multi layer perceptron (MLP)\nneural network was used to classify suspicious regions. Moreover, OB lesions\nwere detected using a rule based image processing method, where no feature\nextraction or classification algorithm were used. The results were validated\nusing a CT dataset of 52 patients, where 22 patients had abnormalities and 30\npatients were normal. Using non-training dataset, CB detection algorithm\nyielded 71% sensitivity with 0.31 false positives per patient. Furthermore, OB\ndetection algorithm achieved 100% sensitivity with 0.13 false positives per\npatient. Results suggest that, the proposed framework, which consists of two\nmethods, has the potential to be used in clinical context, and assist\nradiologists for better diagnosis.","url_abs":"http://arxiv.org/abs/1611.09769v1","url_pdf":"http://arxiv.org/pdf/1611.09769v1.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":"computer-aided-detection-of-oral-lesions-on","repo_url":"https://github.com/smg478/OralCancerDetectionOnCTImages","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"},{"task_slug":"sensitivity","task_name":"Sensitivity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}