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  • Küçük Resim
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    A novel brain tumor magnetic resonance imaging dataset (Gazi Brains 2020): Initial benchmark results and comprehensive analysis
    (PeerJ Inc., 2025) Sagiroğlu, Şeref; Terzi, Ramazan; Çeltikci, Emrah; Börcek, Alp Özgün; Atay, Yılmaz; Arslan, Bilgehan; Şahin, Mustafa Çağlar; Nernekli, Kerem; Demirezen, Umut; Özdemir, Okan Bilge; Özdem Karaca, Kevser; Azgınoğlu, Nuh
    This article presents a new benchmark MRI dataset called the Gazi Brains Dataset 2020, containing MRI images of 100 patients, and introduces initial experimental results performed on this dataset in comparison with available brain MRI datasets. Furthermore, the dataset is analyzed using eight different deep learning models for high-grade glioma tumor prediction, classification, and detection tasks. Additionally, this study demonstrates the results of an explainable Artificial Intelligence (XAI) approach applied to the trained models. To demonstrate the utility of the proposed dataset, different deep learning models were applied to the problem, and these models were tested on various data and models applied for various tasks such as region of interest extraction, whole tumor segmentation, prediction, detection, and classification with accuracy, precision, recall, and F1-score. The experimental results indicate that the dataset is highly effective for multiple purposes, and the models reached significant results with successful F1-scores ranging between 93.2% and 96.4%. ROI and whole tumor segmentations were successfully performed and compared with seven algorithms with accuracies of 87.61% and 97.18%. The Grad-CAM model also demonstrated satisfactory accuracy across the tests that were conducted. Moreover, this study explores the application of XAI to the trained models, providing interpretability and insights into the decision-making processes. The findings signify that this dataset holds significant potential for various future research directions, including age estimation, gender detection, causal inference with XAI, and disease-related survival analysis.
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    The digital eye for mammography: deep transfer learning and model ensemble based open-source toolkit for mass detection and classification
    (Springer Science and Business Media Deutschland GmbH, 2024) Terzi, Ramazan; Kılıç, Ahmet Enes; Karaahmetoğlu, Gökhan; Özdemir, Okan Bilge
    Breast cancer stands as a prevalent malignancy affecting women globally, and a screening method, mammography, boasts reliability for early diagnosis. Nevertheless, interpretive errors during population screening may result in false negatives and positives. To address this, Computer-Aided Detection systems rooted in deep learning have emerged, aiming to reduce both false positive and negative predictions. This study introduces an open-source toolkit called The Digital Eye for Mammography (DEM) and addressing limitations in mammography screening for mass detection and classification. The DEM comprises 11 state-of-the-art object detection architectures and uses a meticulously labeled dataset. It serves as a transfer learning source, and provides ensemble of models from diverse deep-learning architectures, resulting in a more robust solution. Experiments conducted on widely-used datasets indicate that the DEM outperforms existing transfer learning sources by significant margins in terms of true positive rate (TPR). According to the experimental results, the DEM serves as a better transfer learning source for mass detection in pathology-proven InBreast and CBIS-DDSM datasets, presenting improvements 12% and 5% in TPR performance at 0.1 false positive per image (FPPI), respectively. Compared to literature, the DEM achieves lower FPPI values while maintaining higher sensitivity, indicating its potential usage as a transfer learning source. By employing ensemble strategies, the DEM produces more reliable outcomes in our KETEM dataset, reducing FPPI by 49% for BI-RADS 1-2 (Breast Imaging Reporting and Data System) and 46% for BI-RADS 4-5 compared to the best individual model while preserving TPR values. The DEM’s results suggest its ability to attain better performance without requiring complex model hyperparameters optimization. The GitHub repository of the DEM project is publicly available on: https://github.com/ddobvyz/digitaleye-mammography.