Pancreatic tumor detection by convolutional neural networks

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dc.authorid0000-0001-6657-9738
dc.contributor.authorZavalsız, Muhammed Talha
dc.contributor.authorAlhajj, Sleiman
dc.contributor.authorSailunaz, Kashfia
dc.contributor.authorÖzyer, Tansel
dc.contributor.authorAlhajj, Reda
dc.date.accessioned2023-02-14T12:26:06Z
dc.date.available2023-02-14T12:26:06Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.departmentİstanbul Medipol Üniversitesi, Uluslararası Tıp Fakültesi
dc.description.abstractArtificial Intelligence and its sub-branches like Machine Learning (ML) and Deep Learning (DL) applications have the potential to have positive effects that can directly affect human life. Medical imaging provides a way for the internal structure of the human body to be visible with various methods. With DL models, cancer detection, which is one of the most lethal diseases in the world, from medical images can be made possible with high accuracy. The main objective of this paper is to detect Pancreatic Cancer, which is one of the cancer types with the highest fatality rate, from a dataset of Computed Tomography (CT) images, which is one of the medical imaging techniques and has an effective structure in Pancreatic Cancer imaging. The designed DL model is integrated into the Flask application to develop a web application. With this application, early diagnosis of pancreatic cancer can be achieved, which progresses insidiously and therefore does not spread to neighboring tissues and organs when the treatment process is started. Due to the abundance of medical images reviewed by medical professionals, this application can assist radiologists and other specialists in Pancreatic Tumor detection.
dc.identifier.citationZavalsız, M. T., Alhajj, S., Sailunaz, K., Özyer, T. ve Alhajj, R. (2022). Pancreatic tumor detection by convolutional neural networks. 23rd International Arab Conference on Information Technology, ACIT 2022. Abu Dhabi, 22-24 November 2022. https://doi.org/10.1109/ACIT57182.2022.9994181
dc.identifier.doi10.1109/ACIT57182.2022.9994181
dc.identifier.isbn9798350320244
dc.identifier.scopus2-s2.0-85146885143
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/ACIT57182.2022.9994181
dc.identifier.urihttps://hdl.handle.net/20.500.12511/10439
dc.indekslendigikaynakScopus
dc.institutionauthorZavalsız, Muhammed Talha
dc.institutionauthorAlhajj, Sleiman
dc.institutionauthorAlhajj, Reda
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof23rd International Arab Conference on Information Technology, ACIT 2022en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectConvolutional Neural Networks
dc.subjectDeep Learning
dc.subjectFlask
dc.subjectMedical Image
dc.subjectPancreatic Ductal Adenocarcinoma
dc.subjectPancreatic Tumor Detection
dc.titlePancreatic tumor detection by convolutional neural networks
dc.typeConference Object

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