Automated Segmentation of Lung Lesions Using Deep Learning: A Study on a Multi-Class CT Dataset

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dc.contributor.authorKiliç, Murat
dc.contributor.authorYelman, Abdulkadir
dc.contributor.authorÜzen, Hüseyin
dc.contributor.authorFirat, Hüseyin
dc.contributor.authorBiyikli, Merve
dc.contributor.authorBalikçi Çyçek, Ipek
dc.contributor.authorŞengür, Abdulkadir
dc.date.accessioned2026-04-04T13:18:59Z
dc.date.available2026-04-04T13:18:59Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025 -- 6 September 2025 through 7 September 2025 -- Malatya -- 215321
dc.description.abstractLung cancer is the leading cause of cancer-related deaths worldwide, making early diagnosis from CT images critically important for patient outcomes. In this study, a unique dataset was collected at Inönü University Turgut Özal Medical Center, consisting of 3526 CT slices from 210 patients manually annotated by expert physicians. The lesions, classified into benign, malignant, and cystic categories, were segmented using seven different deep learning architectures: U-Net, VGG16UNet, EFF-UNet, UNet++, FPNet, PSPNet, and EFF-PSPNet. The models were evaluated based on metrics such as Dice, Jaccard, precision, recall, and FPS. The most successful results were achieved by the EFF-UNet (Dice: 92.14%) and U-Net (Dice: 92.25%) models. While cystic lesions were detected with high accuracy, benign lesions presented the lowest results due to their morphological variability. Visual analyses indicated that some models exhibited uncertainty in class differentiation, and the quality of annotations was found to directly influence model performance. This study demonstrates the potential of deep learning-based segmentation for clinical decision support systems and contributes to the field with its original dataset. © 2025 IEEE.
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumu, TUBITAK, (125E062); Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TUBITAK
dc.identifier.doi10.1109/IDAP68205.2025.11222129
dc.identifier.isbn979-833158990-5
dc.identifier.scopus2-s2.0-105025056480
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/IDAP68205.2025.11222129
dc.identifier.urihttps://hdl.handle.net/11616/108065
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250329
dc.subjectLung Cancer
dc.subjectSegmentation
dc.subjectUNet
dc.titleAutomated Segmentation of Lung Lesions Using Deep Learning: A Study on a Multi-Class CT Dataset
dc.typeConference Object

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