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Dergiler / Turkish Journal of Forecasting / 2026 / Volume: 07 Issue: 1

Improving Mass Detection in Mammography Using Focal Loss Based RetinaNet

Semih Demirel(Akgün Bilgisayar A.Ş)0000-0002-3454-3631
Ataberk Urfalı(Akgün Bilgisayar A.Ş)0000-0001-5709-6718
Ömer Faruk Bozkır(Akgün Bilgisayar A.Ş)0000-0002-3696-3613
Azer Çelikten(Akgün Bilgisayar A.Ş)0000-0002-6804-737X
Abdulkadir Budak(Akgün Bilgisayar A.Ş)0000-0002-0328-6783
Hakan Karataş(Akgün Bilgisayar A.Ş)0000-0002-9497-5444
Sayfa
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Yayın Tarihi
2023-10-20
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The trained RetinaNet model was integrated into existing PACS (Picture Archiving and Communication System) systems and made ready for use in healthcare centers.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Breast cancer is a significant global health issue and plays a crucial role in improving patient outcomes through early detection.
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Temel Bulgular & Veri:The training and testing were conducted using the Detectron2 platform.
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Sonuç & Katkı:The trained RetinaNet model was integrated into existing PACS (Picture Archiving and Communication System) systems and made ready for use in healthcare centers.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Breast cancer · RetinaNet · Mass detection

Abstract

Breast cancer is a significant global health issue and plays a crucial role in improving patient outcomes through early detection. This study aims to enhance the accuracy and efficiency of breast cancer diagnosis by investigating the application of the RetinaNet and Faster R-CNN algorithms for mass detection in mammography images. A specialized dataset was created for mass detection from mammography images and validated by an expert radiologist. The dataset was trained using RetinaNet and Faster R-CNN, a state-of-the-art object detection model. The training and testing were conducted using the Detectron2 platform. To avoid overfitting during training, data augmentation techniques available in the Detectron2 platform were used. The model was tested using the AP50, precision, recall, and F1-Score metrics. The results of the study demonstrate the success of RetinaNet in mass detection. According to the obtained results, an AP50 value of 0.568 was achieved. The precision and recall performance metrics are 0.735 and 0.60 respectively. The F1-Score metric, which indicates the balance between precision and recall, obtained a value of 0.66. These results demonstrate that RetinaNet can be a potential tool for breast cancer screening and has the potential to provide accuracy and efficiency in breast cancer diagnosis. The trained RetinaNet model was integrated into existing PACS (Picture Archiving and Communication System) systems and made ready for use in healthcare centers.

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Bu Makaleye Atıf Yap

Demirel, S., Urfalı, A., Bozkır, Ö. F., Çelikten, A., Budak, A., Karataş, H. (2023). Improving Mass Detection in Mammography Using Focal Loss Based RetinaNet. Turkish Journal of Forecasting, Volume: 07 Issue: 1. https://doi.org/10.34110/forecasting.1326245

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Kaynakça (27)

  1. K. Loizidou, R. Elia, C. Pitris, Computer-aided breast cancer detection and classification in mammography: A comprehensive review, Computers in Biology and Medicine. 153 (2023) 106554. doi:https://doi.org/10.1016/j.compbiomed.2023.106554
  2. S.J. Frank, A deep learning architecture with an object-detection algorithm and a convolutional neural network for breast mass detection and visualization, Healthcare Analysis. 3 (2023) 100186. doi:https://doi.org/10.1016/j.health.2023.100186
  3. J. Bai, R. Posner, T. Wang, C. Yang, S. Nabavi, Applying deep learning in digital breast tomosynthesis for automatic breast cancer detection: A review, Medical Image Analysis. 71 (2021) 102049. doi:https://doi.org/10.1016/j.media.2021.102049
  4. L. Abdelrahman, M.A. Ghamdi, F.C. Mesa, M.A. Mottaleb, Convolutional neural networks for breast cancer detection in mammography: A survey, Computers in Biology and Medicine. 131 (2021) 104248. doi:https://doi.org/10.1016/j.compbiomed.2021.104248
  5. L. Garrucho, K. Kushibar, S. Jouide, O. Diaz, L. Igual, K. Lekadir, Domain generalization in deep learning based mass detection in mammography: A large-scale multi-center study, Artificial Intelligence in Medicine. 132 (2022) 102386. doi:https://doi.org/10.1016/j.artmed.2022.102386
  6. A. Baccouche, B.G. Zapirain, Y. Zheng, A.S. Elmaghraby, Early detection and classification of abnormality in prior mammograms using image-to-image translation and YOLO techniques, Computer Methods and Programs in Biomedicine. 221 (2022) 106884. doi:https://doi.org/10.1016/j.cmpb.2022.106884
  7. S. Famouri, L. Morra, L. Mangia, F. Lamberti, Breast mass detection with faster r-cnn: On the feasibility of learning from noisy annotations, IEEE Access. 9 (2021) 66163-66175. doi:10.1109/ACCESS.2021.3072997
  8. S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: Towards real-time object detection with region proposal networks, IEEE Transactions on Pattern Analysis and Machine Intelligence. 39 (2016) 1137-1149. doi:10.1109/TPAMI.2016.2577031
  9. M. W. A. El-Soud, I. Zyout, K. M. Hosny, M. M. Eltoukhy, Fusion of orthogonal moment features for mammographic mass detection and diagnosis, IEEE Access. 8 (2020) 129911-129923. doi:10.1109/ACCESS.2020.3008038
  10. S. Imran, B. A. Lodhi, A. Alzahrani, Unsupervised method to localize masses in mammograms, IEEE Access. 9 (2021) 99327-99338. doi:10.1109/ACCESS.2021.3094768
  11. G. Toz, P. Erdoğmuş, A novel hybrid image segmentation method for detection of suspicious regions in mammograms based on adaptive multi-thresholding (HCOW), IEEE Access. 9 (2021) 85377-85391. doi:10.1109/ACCESS.2021.3089077
  12. S. Kumbhare, A.B. Kathole, and S. Shinde, Federated-learning aided breast cancer detection with intelligent heuristic-based deep learning framework, Biomedical Signal Processing and Control. 86 (2023) 105080. doi: https://doi.org/10.1016/j.bspc.2023.105080
  13. G. Huang, Z. Liu, L.V.D. Maaten, K.Q. Weinberger, Densely connected convolutional networks, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017: pp. 2261-2269. doi: 10.1109/CVPR.2017.243
  14. M. Zhang, C. Wang, L. Cai, J. Zhao, Y. Xu, J. Xing, J. Sun, Y. Zhang, Developing a weakly supervised deep learning framework for breast cancer diagnosis with HR status based on mammography images, Computational and Structural Biotechnology Journal. 22 (2023) 17-36. doi: https://doi.org/10.1016/j.csbj.2023.08.012
  15. J. Wu, Z. Xu, L. Shang, Z. Wang, S. Zhou, H. Shang, H. Wang, J. Yin, Multimodal microscopic imaging with deep learning for highly effective diagnosis of breast cancer, Optics and Lasers in Engineering. 168 (2023) 107667. doi: https://doi.org/10.1016/j.optlaseng.2023.107667
  16. B. Asadi, Q. Memon, Efficient breast cancer detection via cascade deep learning network, International Journal of Intelligent Networks. 4 (2023) 46-52. doi: https://doi.org/10.1016/j.ijin.2023.02.001
  17. O. Ronneberger, P. Fischer, T. Brox, U-Net Convolutional Networks for Biomedical Image Segmentation, in: Medical Image Computing and Computer-Assisted Intervention MICCAI 2015, 2015: pp. 234-241. doi: https://doi.org/10.1007/978-3-319-24574-4_28
  18. K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: pp. 770-778. doi:10.1109/CVPR.2016.90
  19. R. S. Raaj, Breast cancer detection and diagnosis using hybrid deep learning architecture, Biomedical Signal Processing and Control. 82 (2023) 104558. doi: https://doi.org/10.1016/j.bspc.2022.104558
  20. Q. Lin, W.M. Tan, J.Y. Ge, Y. Huang, Q. Xiao, Y.Y. Xu, Y.T. Jin, Z.M. Shao, Y.J. Gu, B. Yan, K.D. Yu, Artificial intelligence-based diagnosis of breast cancer by mammography microcalcification, Fundamental Research. (2023).
  21. T. Y. Lin, P. Goyal, R. Girshick, K. He, P. Dollar, Focal loss for dense object detection, in: 2017 IEEE International Conference on Computer Vision (ICCV), 2017: pp. 2999-3007. doi:10.1109/TPAMI.2018.2858826
  22. G. Hamed, M. Marey, S. E. Amin, M. F. Tolba, Automated breast cancer detection and classification in full field digital mammograms using two full and cropped detection paths approach, IEEE Access. 9 (2021) 116898-116913. doi:10.1109/ACCESS.2021.3105924
  23. T. Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, S. Belongie, Feature pyramid networks for object detection, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017: pp. 936-944. doi:10.1109/CVPR.2017.106
  24. T. Miao, H. Zeng, W. Yang, B. Chu, F. Zou, W. Ren, J. Chen, An improved lightweight retinanet for ship detection in SAR images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15 (2022) 4667-4679. doi:10.1109/JSTARS.2022.3180159
  25. J. Liu, R. Jia, W. Li, F. Ma, H.M. Abdullah, H.Ma, M.A. Mohamed, High precision detection algorithm based on improved RetinaNet for defect recognition of transmission lines, Energy Reports. 6 (2020) 2430-2440. doi: https://doi.org/10.1016/j.egyr.2020.09.002
  26. H. Peng, Z. Li, Z. Zhou, Y. Shao, Weed detection in paddy field using an improved RetinaNet network, Computers and Electronics in Agriculture. 199 (2022) 107179. doi: https://doi.org/10.1016/j.compag.2022.107179
  27. R. Viola, L. Gautheron, A. Habrard, M. Sebban, MetaAP: A meta-tree-based ranking algorithm optimizing the average precision from imbalanced data, Pattern Recognition Letters. 161 (2022) 161-167. doi: https://doi.org/10.1016/j.patrec.2022.07.019