Deep Learning-Assisted Detection and Classification of Thymoma Tumors in CT Scans

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dc.contributor.authorKilic, Murat
dc.contributor.authorBiyikli, Merve
dc.contributor.authorOzcelik, Salih Taha Alperen
dc.contributor.authorUzen, Huseyin
dc.contributor.authorFirat, Huseyin
dc.date.accessioned2026-04-04T13:31:09Z
dc.date.available2026-04-04T13:31:09Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractBackground/Objectives: Thymoma is a rare epithelial neoplasm originating from the thymus gland, and its accurate detection and classification using computed tomography (CT) images remain diagnostically challenging due to subtle morphological similarities with other mediastinal pathologies. This study presents a deep learning (DL)-based model designed to improve diagnostic accuracy for both thymoma detection and subtype classification (benign vs. malignant). Methods: The proposed approach integrates a pre-trained VGG16 network for efficient feature extraction-capitalizing on its capacity to capture hierarchical spatial features-and an MLP-Mixer-based feature enhancement module, which effectively models both local and global feature dependencies without relying on conventional convolutional mechanisms. Additionally, customized preprocessing and post-processing methods are employed to enhance image quality and suppress redundant data. The model's performance was evaluated on two classification tasks: distinguishing thymoma from healthy cases and discriminating between benign and malignant thymoma. Comparative analysis was conducted against state-of-the-art DL models including ResNet50, ResNet34, SEResNeXt50, InceptionResNetV2, MobileNetV2, VGG16, InceptionV3, and DenseNet121 using metrics such as F1 score, accuracy, recall, and precision. Results: The model proposed in this study obtained its best performance in thymoma vs. healthy classification, with an accuracy of 97.15% and F1 score of 80.99%. In the benign vs. malignant task, it attained an accuracy of 79.20% and an F1 score of 78.51%, outperforming all baseline methods. Conclusions: The integration of VGG16's robust spatial feature extraction and the MLP-Mixer's effective feature mixing demonstrates superior and balanced performance, highlighting the model's potential for clinical decision support in thymoma diagnosis.
dc.identifier.doi10.3390/diagnostics15243191
dc.identifier.issn2075-4418
dc.identifier.issue24
dc.identifier.orcid0000-0002-7929-7542
dc.identifier.orcid0000-0001-7500-8478
dc.identifier.orcid0009-0005-0239-0393
dc.identifier.orcid0000-0002-0998-2130
dc.identifier.orcid0000-0002-1257-8518
dc.identifier.pmid41464191
dc.identifier.scopus2-s2.0-105025933559
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/diagnostics15243191
dc.identifier.urihttps://hdl.handle.net/11616/108606
dc.identifier.volume15
dc.identifier.wosWOS:001647371500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofDiagnostics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectthymoma disease
dc.subjectMLP-Mixer
dc.subjectVGG16
dc.subjectcomputed tomography
dc.titleDeep Learning-Assisted Detection and Classification of Thymoma Tumors in CT Scans
dc.typeArticle

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