{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T10:29:03Z","timestamp":1765794543334,"version":"3.48.0"},"reference-count":32,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,14]],"date-time":"2025-12-14T00:00:00Z","timestamp":1765670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/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\u2014capitalizing on its capacity to capture hierarchical spatial features\u2014and 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\u2019s 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\u2019s robust spatial feature extraction and the MLP-Mixer\u2019s effective feature mixing demonstrates superior and balanced performance, highlighting the model\u2019s potential for clinical decision support in thymoma diagnosis.<\/jats:p>","DOI":"10.3390\/diagnostics15243191","type":"journal-article","created":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T10:11:23Z","timestamp":1765793483000},"page":"3191","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Assisted Detection and Classification of Thymoma Tumors in CT Scans"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7500-8478","authenticated-orcid":false,"given":"Murat","family":"K\u0131l\u0131\u00e7","sequence":"first","affiliation":[{"name":"Department of Thoracic Surgery, Faculty of Medicine, Inonu University, Malatya 44210, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0239-0393","authenticated-orcid":false,"given":"Merve","family":"B\u0131y\u0131kl\u0131","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Faculty of Medicine, Inonu University, Malatya 44210, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7929-7542","authenticated-orcid":false,"given":"Salih Taha Alperen","family":"\u00d6z\u00e7elik","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Faculty of Engineering, Bing\u00f6l University, Bing\u00f6l 12000, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0998-2130","authenticated-orcid":false,"given":"H\u00fcseyin","family":"\u00dczen","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering and Architecture, Bingol University, Bing\u00f6l 12000, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1257-8518","authenticated-orcid":false,"given":"H\u00fcseyin","family":"F\u0131rat","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Dicle University, Diyarbak\u0131r 21280, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1148\/rg.262045213","article-title":"The Thymus: A Comprehensive Review","volume":"26","author":"Nishino","year":"2006","journal-title":"Radiographics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1148\/radiology.210.1.r99ja4511","article-title":"The Right Place at the Wrong Time: Historical Perspective of the Relation of the Thymus Gland and Pediatric Radiology","volume":"210","author":"Jacobs","year":"1999","journal-title":"Radiology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/S1052-3359(25)00570-8","article-title":"The Thymus. 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