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dc.contributor.authorBicakci, Mustafa
dc.contributor.authorAyyildiz, Oguzhan
dc.contributor.authorAydin, Zafer
dc.contributor.authorBasturk, Alper
dc.contributor.authorKaracavus, Seyhan
dc.contributor.authorYilmaz, Bulent
dc.date.accessioned2021-01-18T11:14:19Z
dc.date.available2021-01-18T11:14:19Z
dc.date.issued2020en_US
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/20.500.12573/454
dc.descriptionThis work was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under Project 113E188.en_US
dc.description.abstractLung cancer is one of the deadliest cancer types whose 84% is non-small cell lung cancer (NSCLC). In this study, deep learning-based classification methods were investigated comprehensively to differentiate two subtypes of NSCLC, namely adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). The study used 1457 F-18-FDG PET images/slices with tumor from 94 patients (88 men), 38 of which were ADC and the rest were SqCC. Three experiments were carried out to examine the contribution of peritumoral areas in PET images on subtype classification of tumors. We assessed multilayer perceptron (MLP) and three convolutional neural network (CNN) models such as SqueezeNet, VGG16 and VGG19 using three kinds of images in these experiments: 1) Whole slices without cropping or segmentation, 2) cropped image portions (square subimages) that include the tumor and 3) segmented image portions corresponding to tumors using random walk method. Several optimizers and regularization methods were used to optimize each model for the diagnostic classification. The classification models were trained and evaluated by performing stratified 10-fold cross validation, and F-score and area-under-curve (AUC) metrics were used to quantify the performance. According to our results, it is possible to say that inclusion of peritumoral regions/tissues both contributes to the success of models and makes segmentation effort unnecessary. To the best of our knowledge, deep learning-based models have not been applied to the subtype classification of NSCLC in PET imaging, therefore, this study is a significant cornerstone providing thorough comparisons and evaluations of several deep learning models on metabolic imaging for lung cancer. Even simpler deep learning models are found promising in this domain, indicating that any improvement in deep learning models in machine learning community can be reflected well in this domain as well.en_US
dc.description.sponsorshipurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) 113E188en_US
dc.language.isoengen_US
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USAen_US
dc.relation.isversionof10.1109/ACCESS.2020.3040155en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectnon-small cell lung canceren_US
dc.subjectsubtype classificationen_US
dc.subjectPET imagingen_US
dc.subjectdeep learningen_US
dc.subjectConvolutional neural networksen_US
dc.subjectPositron emission tomographyen_US
dc.subjectStandardsen_US
dc.subjectCanceren_US
dc.subjectImagingen_US
dc.subjectLung canceren_US
dc.subjectTumorsen_US
dc.titleMetabolic Imaging Based Sub-Classification of Lung Canceren_US
dc.typearticleen_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümüen_US
dc.contributor.authorID0000-0001-5810-0643en_US
dc.identifier.volumeVolume: 8en_US
dc.identifier.startpage218470en_US
dc.identifier.endpage218476en_US
dc.relation.journalIEEE ACCESSen_US
dc.relation.tubitak113E188
dc.relation.publicationcategoryMakale - Uluslararası - Editör Denetimli Dergien_US


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