Göğüs Kanseri Kitle Tespitinde Derin Öğrenme Modellerinin Karşılaştırılması: YOLOv8 ve U-Net

Öz

Hastalıkların erken tespiti, özellikle kanser gibi hayati risk taşıyan durumlarda, tedavi sürecinin başarısı için kritik öneme sahiptir. Göğüs kanseri gibi hastalıklarda erken dönemde yapılan kitle tespiti, tedavi sürecinin etkinliği açısından belirleyici olabilir. Bu çalışma, göğüs görüntülerindeki kitle tespiti amacıyla YOLOv8 ve U-Net modellerinin performanslarını karşılaştırmıştır. İlk aşamada, her iki model de CBIS-DDSM ve INbreast veri setleri üzerinde değerlendirilmiştir. Elde edilen sonuçlar, YOLOv8 modelinin precision metriklerinde U-Net'e kıyasla daha yüksek performans gösterdiğini ortaya koymuştur. CBIS-DDSM veri setinde YOLOv8 0.800123 precision değeri elde ederken, U-Net 0.762345 değerine ulaşmıştır. INbreast veri setinde ise YOLOv8 0.785234 precision değerine sahipken, U-Net 0.742345 değerini elde etmiştir. Bu bulgular, YOLOv8'in özellikle nesne tespiti görevlerinde daha başarılı ve hızlı sonuçlar verdiğini, tıbbi görüntüleme gibi hızlı kararlar alınması gereken alanlarda daha verimli olduğunu göstermektedir. Gelecekteki çalışmalar, her iki modelin güçlü yönlerini birleştirerek hibrit çözümler geliştirebilir ve model hızlarını optimize ederek tıbbi teşhislerde daha hızlı ve doğru sonuçlar elde edilmesini sağlayabilir.

Anahtar Kelimeler

• Göğüs Kanseri
• Kitle Tespiti
• Derin Öğrenme
• YOLOv8
• U-Net.

Comparison of Deep Learning Models for Breast Cancer Mass Detection: YOLOv8 and U-Net

Öz

Early detection of diseases is critical to the success of the treatment process, especially in life-threatening conditions such as cancer. In diseases such as breast cancer, early mass detection can be decisive for the effectiveness of the treatment process. This study compares the performance of YOLOv8 and U-Net models for mass detection in breast images. In the first stage, both models are evaluated on CBIS-DDSM and INbreast datasets. The results show that the YOLOv8 model outperforms U-Net in precision metrics. In the CBIS-DDSM dataset, YOLOv8 achieved a precision value of 0.800123, while U-Net achieved 0.762345. In the INbreast dataset, YOLOv8 achieved a precision value of 0.785234, while U-Net achieved a value of 0.742345. These findings show that YOLOv8 provides more successful and faster results, especially in object detection tasks, and is more efficient in areas where fast decisions need to be made, such as medical imaging. Future studies can develop hybrid solutions by combining the strengths of both models and optimize model speeds to achieve faster and more accurate results in medical diagnostics.

Anahtar Kelimeler

• Breast Cancer
• Mass Detection
• Deep Learning
• YOLOv8
• U-Net.

Kaynakça

1. Aly, G. H., Marey, M., El-Sayed, S. A., & Tolba, M. F. 2021. YOLO based breast masses detection and classification in full-field digital mammograms. Computer Methods and Programs in Biomedicine, 200, 105823.
2. Al-Masni, M. A., Al-Antari, M. A., Park, J. M., Gi, G., Kim, T. Y., Rivera, P., ... & Kim, T. S. 2018. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Computer Methods and Programs in Biomedicine, 157, 85-94.
3. Baccouche, A., Garcia-Zapirain, B., Olea, C. C., & Elmaghraby, A. S. 2021. Breast Lesions Detection and Classification via YOLO-Based Fusion Models. Computers, Materials & Continua, 69(1).
4. Baccouche, A., Garcia-Zapirain, B., Zheng, Y., & Elmaghraby, A. S. 2022. Early detection and classification of abnormality in prior mammograms using image-to-image translation and YOLO techniques. Computer Methods and Programs in Biomedicine, 221, 106884.
5. Bleyer, A., & Welch, H. G. 2012. Effect of three decades of screening mammography on breast-cancer incidence. New England Journal of Medicine, 367(21), 1998-2005.
6. Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. 2020. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.
7. Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. 2016. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19 (pp. 424-432). Springer International Publishing.
8. DeSantis, C. E., Ma, J., Gaudet, M. M., Newman, L. A., Miller, K. D., Goding Sauer, A., ... & Siegel, R. L. 2019. Breast cancer statistics, 2019. CA: a cancer journal for clinicians, 69(6), 438-451.

9. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
10. Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. 2010. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88, 303-338.
11. Giaquinto, A. N., Sung, H., Newman, L. A., Freedman, R. A., Smith, R. A., Star, J., ... & Siegel, R. L. 2024. Breast cancer statistics 2024. CA: a cancer journal for clinicians, 74(6), 477-495.
12. Goodfellow, I. 2016. Deep learning (Vol. 196). MIT press.
13. He, K., Zhang, X., Ren, S., & Sun, J. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
14. Hamed, G., Marey, M., Amin, S. E., & Tolba, M. F. 2021. Automated breast cancer detection and classification in full field digital mammograms using two full and cropped detection paths approach. IEEE Access, 9, 116898-116913.
15. Houssein, E. H., Emam, M. M., & Ali, A. A. 2022. An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm. Neural Computing and Applications, 34(20), 18015-18033.
16. Hu, C. T., Matsushima, A., Huang, Y. H., Okamoto, T., Liu, K. Y., Hsu, S. Y., & Chen, T. B. 2023. Harnessing YOLO algorithms for efficient breast cancer detection in mammography. Journal of Medical Imaging and Radiation Sciences, 54(3), S7.
17. Humphrey, L. L., Helfand, M., Chan, B. K., & Woolf, S. H. 2002. Breast cancer screening: a summary of the evidence for the US Preventive Services Task Force. Annals of internal medicine, 137(5_Part_1), 347-360.
18. Ibtehaz, N., & Rahman, M. S. 2020. MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Networks, 121, 74-87.
19. Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. 2016. Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759.
20. Lee, R. S., Gimenez, F., Hoogi, A., Miyake, K. K., Gorovoy, M., & Rubin, D. L. 2017. A curated mammography data set for use in computer-aided detection and diagnosis research. Scientific Data, 4(1), 1-9.
21. Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., ... & Zitnick, C. L. 2014. Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13 (pp. 740-755). Springer International Publishing.
22. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. 2017. A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.
23. Mohammed, A. D., & Ekmekci, D. 2024. Breast Cancer Diagnosis Using YOLO-Based Multiscale Parallel CNN and Flattened Threshold Swish. Applied Sciences, 14(7), 2680.
24. Moreira, I. C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M. J., & Cardoso, J. S. 2012. Inbreast: toward a full-field digital mammographic database. Academic Radiology, 19(2), 236-248.
25. Nasser, M., & Yusof, U. K. 2023. Deep learning based methods for breast cancer diagnosis: a systematic review and future direction. Diagnostics, 13(1), 161.
26. Prinzi, F., Insalaco, M., Orlando, A., Gaglio, S., & Vitabile, S. 2024. A YOLO-based model for breast cancer detection in mammograms. Cognitive Computation, 16(1), 107-120.
27. Quiñones-Espín, A. E., Perez-Diaz, M., Espín-Coto, R. M., Rodriguez-Linares, D., & Lopez-Cabrera, J. D. 2023. Automatic detection of breast masses using deep learning with YOLO approach. Health and Technology, 13(6), 915-923.
28. Rasheed, A. F., & Zarkoosh, M. 2025. Optimized YOLOv8 for multi-scale object detection. Journal of Real-Time Image Processing, 22(1), 6.
29. Redmon, J. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition.
30. Ronneberger, O., Fischer, P., & Brox, T. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 (pp. 234-241). Springer International Publishing.
31. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., ... & Fei-Fei, L. 2015. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115, 211-252.
32. Shorten, C., & Khoshgoftaar, T. M. 2019. A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), 1-48.
33. Su, Y., Liu, Q., Xie, W., & Hu, P. 2022. YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms. Computer Methods and Programs in Biomedicine, 221, 106903.
34. Talib, M., Al-Noori, A. H., & Suad, J. 2024. YOLOv8-CAB: Improved YOLOv8 for Real-time object detection. Karbala International Journal of Modern Science, 10(1), 5.
35. Wang, J., & Perez, L. 2017. The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Networks Vis. Recognit, 11(2017), 1-8.
36. Wang, J., Azziz, A., Fan, B., Malkov, S., Klifa, C., Newitt, D., ... & Shepherd, J. A. 2013. Agreement of mammographic measures of volumetric breast density to MRI. PloS One, 8(12), e81653.
37. Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. 2018. Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Proceedings 4 (pp. 3-11). Springer International Publishing.