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RADYOMİK ÖZELLİKLER VE MAKİNE ÖĞRENMESİ TEKNİKLERİYLE MEME TÜMÖRLERİNİN SINIFLANDIRILMASI

Year 2025, Volume: 28 Issue: 1, 38 - 50, 03.03.2025

Asuman Kaplan , Esra Kavadar , Mehmet Ali Altuncu

Abstract

Meme kanseri, dünya genelinde kadınlar arasında en sık görülen kanser türüdür ve erken teşhis, tedavi başarısını önemli ölçüde artırmaktadır. Bu çalışmada, meme ultrason görüntülerinden iyi huylu ve kötü huylu tümörleri sınıflandırmak amacıyla radyomik özellikler ve makine öğrenmesi teknikleri kullanılmıştır. Çalışmada, halka açık BUSI veri seti kullanılmıştır. Sadece iyi huylu ve kötü huylu olarak etiketlenmiş görüntüler sınıflandırmada kullanılmış olup, normal etiketli görüntüler çalışmaya dahil edilmemiştir. Bu yaklaşım, modelin iki sınıf arasındaki ayrımı en yüksek doğrulukla yapmasına odaklanmıştır. Veri setindeki dengesizlik, kötü huylu tümörlerin görüntülerinin y ekseninde aynalanarak artırılmasıyla giderilmiştir. PyRadiomics kütüphanesi ile çıkarılan 123 radyomik özellik arasından, özellik önem skoru ve korelasyon matrisi kullanılarak en önemli 40 özellik seçilmiştir. Sınıflandırma aşamasında XGBoost, Gradient Boosting, AdaBoost, SVM, Random Forest ve Decision Tree algoritmaları uygulanmış, en yüksek doğruluk oranı (%98.13) Gradient Boosting algoritması ile elde edilmiştir.

Keywords

Makine öğrenmesi meme kanseri radyomik özellikler tümör sınıflandırma

References

  • Adnan, M., Alarood, A. a. S., Uddin, M. I., & Rehman, I. U. (2022). Utilizing grid search cross-validation with adaptive boosting for augmenting performance of machine learning models. PeerJ. Computer Science, 8, e803. https://doi.org/10.7717/peerj-cs.803
  • Al-Dhabyani, W., Gomaa, M., Khaled, H., & Fahmy, A. (2020). Dataset of breast ultrasound images. Data in Brief, 28, 104863. https://doi.org/10.1016/j.dib.2019.104863
  • Ali, J., Khan, R., Ahmad, N., & Maqsood, I. (2012). Random forests and decision trees. International Journal of Computer Science Issues, 9(5), 272–278. http://ijcsi.org/papers/IJCSI-9-5-3-272-278.pdf
  • Ara, S., Das, A., & Dey, A. (2021, April). Malignant and benign breast cancer classification using machine learning algorithms. In 2021 International Conference on Artificial Intelligence (ICAI) (pp. 97-101). IEEE.
  • Ardakani, A. A., Bureau, N. J., Ciaccio, E. J., & Acharya, U. R. (2022). Interpretation of radiomics features–A pictorial review. Computer Methods and Programs in Biomedicine, 215, 106609. https://doi.org/10.1016/j.cmpb.2021.106609
  • Aristokli, N., Polycarpou, I., Themistocleous, S. C., Sophocleous, D., & Mamais, I. (2022). Comparison of the diagnostic performance of Magnetic Resonance Imaging (MRI), ultrasound and mammography for detection of breast cancer based on tumor type, breast density and patient's history: A review. Radiography, 28(3), 848–856. https://doi.org/10.1016/j.radi.2022.01.006
  • Assiri, A. S., Nazir, S., & Velastin, S. A. (2020). Breast tumor classification using an ensemble machine learning method. Journal of Imaging, 6(6), 39. https://doi.org/10.3390/jimaging6060039
  • Badawy, S. M., Mohamed, A. E. N. A., Hefnawy, A. A., Zidan, H. E., GadAllah, M. T., & El-Banby, G. M. (2021, July). Classification of Breast Ultrasound Images Based on Convolutional Neural Networks-A Comparative Study. In 2021 International Telecommunications Conference (ITC-Egypt) (pp. 1-8). IEEE.
  • Bota, M. A., Gota, D. I., Bota, P., Stan, O. P., Pop, A., Fanca, A., ... & Miclea, L. (2024, February). Utilizing Transfer Learning-Based Algorithms for Breast Ultrasound Data in Multi-Instance Classification. In 2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) (pp. 1-6). IEEE.
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  • Çapkan, H., Dönmez, B., Kalkan, G. M., Kaya, M. Z., Gürel, S., Akdağlı, E., ... & Uçar, M. K. (2022). Diagnosis of Breast Cancer with Hybrid Artificial Intelligence Method. Avrupa Bilim ve Teknoloji Dergisi, (42), 14-19.
  • Eroğlu, Y., Yildirim, M., & Çinar, A. (2021). Convolutional Neural Networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR. Computers in Biology and Medicine, 133, 104407. https://doi.org/10.1016/j.compbiomed.2021.104407
  • Fornacon-Wood, I., Mistry, H., Ackermann, C. J., Blackhall, F., McPartlin, A., Faivre-Finn, C., ... & O’Connor, J. P. (2020). Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform. European Radiology, 30(12), 6241-6250. https://doi.org/10.1007/s00330-020-06957-9
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  • Ghabrim, H., Essid, C., & Sakli, H. (2023, February). A diagnostic system for classifying and segmenting breast cancer based on ultrasound images. In 2023 20th International Multi-Conference on Systems, Signals & Devices (SSD) (pp. 919-924). IEEE.
  • Gupta, S., Panwar, A., Yadav, R., Aeri, M., & Manwal, M. (2022, February). Employing Deep Learning Feature Extraction Models with Learning Classifiers to Diagnose Breast Cancer in Medical Images. In 2022 IEEE Delhi Section Conference (DELCON) (pp. 1-6). IEEE.
  • Hussain, Z., Gimenez, F., Yi, D., & Rubin, D. (2017). Differential data augmentation techniques for medical imaging classification tasks. AMIA annual symposium proceedings, 2017, 979-984.
  • Jabeen, K., Khan, M. A., Alhaisoni, M., Tariq, U., Zhang, Y. D., Hamza, A., Khalid, A., Kumar, S., Awais, M., Ali, M., & Damaševičius, R. (2022). Breast cancer classification from ultrasound images using probability-based optimal deep learning feature fusion. Sensors, 22(3), 807. https://doi.org/10.3390/s22030807
  • Khanna, P., Sahu, M., & Singh, B. K. (2021, December). Improving the classification performance of breast ultrasound image using deep learning and optimization algorithm. In 2021 IEEE International Conference on Technology, Research, and Innovation for Betterment of Society (TRIBES) (pp. 1-6). IEEE.
  • Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., Patil, D. C., Little, R. B., Tsien, C. I., & Gillies, R. J. (2012). Radiomics: The process and the challenges. Magnetic Resonance Imaging, 30(9), 1234-1248. https://doi.org/10.1016/j.mri.2012.06.010
  • Lanjewar, M. G., Panchbhai, K. G., & Patle, L. B. (2024). Fusion of transfer learning models with LSTM for detection of breast cancer using ultrasound images. Computers in Biology and Medicine, 169, 107914. https://doi.org/10.1016/j.compbiomed.2023.107914
  • LIFEx. (n.d.). LIFEx: Lesion imaging feature extraction. Retrieved September 4, 2024, from https://www.lifexsoft.org/
  • Liu, H., Cui, G., Luo, Y., Guo, Y., Zhao, L., Wang, Y., . . . Tuncer, T. (2022). Artificial Intelligence-Based breast cancer diagnosis using ultrasound images and Grid-Based Deep Feature Generator. International Journal of General Medicine, Volume 15, 2271–2282. https://doi.org/10.2147/ijgm.s347491
  • Luo, J., Quan, Y., & Xu, S. (2024). Robust-GBDT: GBDT with nonconvex loss for tabular classification in the presence of label noise and class imbalance. arXiv. https://arxiv.org/abs/2310.05067
  • Medical Imaging Interaction Toolkit. (n.d.). The Medical Imaging Interaction Toolkit (MITK). Retrieved September 4, 2024, from https://www.mitk.org/wiki/The_Medical_Imaging_Interaction_Toolkit_(MITK)
  • Mishra, A. K., Roy, P., Bandyopadhyay, S., & Das, S. K. (2021). Breast ultrasound tumour classification: A Machine Learning—Radiomics based approach. Expert Systems, 38(7). https://doi.org/10.1111/exsy.12713
  • Myles, A. J., Feudale, R. N., Liu, Y., Woody, N. A., & Brown, S. D. (2004). An introduction to decision tree modeling. Journal of Chemometrics, 18(6), 275–285. https://doi.org/10.1002/cem.873
  • Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7, 21. https://doi.org/10.3389/fnbot.2013.00021
  • Nielsen, D. (2016). Tree Boosting With XGBoost. Master's thesis. Norwegian University of Science and Technology, Trondheim 98s.
  • Oshiro, T. M., Perez, P. S., & Baranauskas, J. A. (2012). How many trees in a random forest?. In Machine Learning and Data Mining in Pattern Recognition: 8th International Conference, MLDM 2012, Berlin, Germany, July 13-20, 2012. Proceedings 8 (pp. 154-168). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_13
  • Pesapane, F., De Marco, P., Rapino, A., Lombardo, E., Nicosia, L., Tantrige, P., Sardanelli, F., Manfredi, R., Bertelli, E., Castello, R., & Cassano, E. (2023). How radiomics can improve breast cancer diagnosis and treatment. Journal of Clinical Medicine, 12(4), 1372. https://doi.org/10.3390/jcm12041372
  • Pisner, D. A., & Schnyer, D. M. (2020). Support vector machine. In Machine learning (pp. 101-121). Academic Press. https://doi.org/10.1016/B978-0-12-815739-8.00006-7
  • Podda, A. S., Balia, R., Barra, S., Carta, S., Fenu, G., & Piano, L. (2022). Fully-automated deep learning pipeline for segmentation and classification of breast ultrasound images. Journal of Computational Science, 63, 101816. https://doi.org/10.1016/j.jocs.2022.101816
  • Pourasad, Y., Zarouri, E., Parizi, M. S., & Mohammed, A. S. (2021). Presentation of novel architecture for diagnosis and identifying breast cancer location based on ultrasound images using Machine learning. Diagnostics, 11(10), 1870. https://doi.org/10.3390/diagnostics11101870
  • Quinlan, J. R. (1996). Learning decision tree classifiers. ACM Computing Surveys (CSUR), 28(1), 71-72. Rashid, H. U., Ibrikci, T., Paydaş, S., Binokay, F., & Çevik, U. (2022). Analysis of breast cancer classification robustness with radiomics feature extraction and deep learning techniques. Expert Systems, 39(8), e13018. https://doi.org/10.1111/exsy.13018
  • Rizzo, S., Botta, F., Raimondi, S., Origgi, D., Fanciullo, C., Morganti, A. G., & Bellomi, M. (2018). Radiomics: the facts and the challenges of image analysis. European Radiology Experimental, 2(1). https://doi.org/10.1186/s41747-018-0068-z
  • Şenol, A., & Kaya, M. (2024). An investigation on the use of clustering algorithms for data preprocessing in breast cancer diagnosis. Türk Doğa ve Fen Dergisi, 13(1), 70-77. https://doi.org/10.46810/tdfd.1364397 Schapire, R. E. (2013). Explaining AdaBoost. In Springer eBooks (pp. 37–52). https://doi.org/10.1007/978-3-642-41136-6_5
  • Sun, H., Li, H., Si, S., Qi, S., Zhang, W., Ma, H., Chen, X., Liu, Y., Yang, J., Zhang, X., & Qian, W. (2018). Performance evaluation of breast cancer diagnosis with mammography, ultrasonography, and magnetic resonance imaging. Journal of X-ray Science and Technology, 26(5), 805-813. doi: https://doi.org/10.3233/xst-18388
  • Van Griethuysen, J. J., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., . . . Aerts, H. J. (2017). Computational Radiomics system to decode the radiographic phenotype. Cancer Research, 77(21), e104–e107. https://doi.org/10.1158/0008-5472.can-17-0339
  • World Health Organization (WHO). (2024, Eylül 4). Breast cancer. https://www.who.int/news-room/fact-sheets/detail/breast-cancer
  • Zhang, G., Zhao, K., Hong, Y., Qiu, X., Zhang, K., & Wei, B. (2021). SHA-MTL: Soft and hard attention multi-task learning for automated breast cancer ultrasound image segmentation and classification. International Journal of Computer Assisted Radiology and Surgery, 16(12), 1719-1725. https://doi.org/10.1007/s11548-021-02445-7
  • Zhang, W., Guo, Y., & Jin, Q. (2023). Radiomics and its feature selection: A review. Symmetry, 15(10), 1834. https://doi.org/10.3390/sym15101834
  • Zhang, X., Zhang, Y., Zhang, G., Qiu, X., Tan, W., Yin, X., & Liao, L. (2022). Deep learning with radiomics for disease diagnosis and treatment: Challenges and potential. Frontiers in Oncology, 12, 773840. https://doi.org/10.3389/fonc.2022.773840

CLASSIFICATION OF BREAST TUMORS USING RADIOMIC FEATURES AND MACHINE LEARNING TECHNIQUES

Year 2025, Volume: 28 Issue: 1, 38 - 50, 03.03.2025

Asuman Kaplan , Esra Kavadar , Mehmet Ali Altuncu

Abstract

Breast cancer is the most common type of cancer among women worldwide, and early diagnosis significantly increases the success of treatment. In this study, radiomic features and machine learning techniques were used to classify benign and malignant tumors from breast ultrasound images. The publicly available BUSI dataset was used in the study. Only images labeled as benign and malignant were used in the classification, and normal labeled images were not included in the study. This approach focused on the model distinguishing between the two classes with the highest accuracy. The imbalance in the dataset was eliminated by mirroring and augmenting the images of malignant tumors in the y-axis. Among the 123 radiomic features extracted with the PyRadiomics library, the most important 40 features were selected using feature importance scores and correlation matrix. XGBoost, Gradient Boosting, AdaBoost, SVM, Random Forest and Decision Tree algorithms were applied in the classification phase, and the highest accuracy rate (98.13%) was obtained with the Gradient Boosting algorithm.

Keywords

Machine learning breast cancer radiomics tumor classification

References

  • Adnan, M., Alarood, A. a. S., Uddin, M. I., & Rehman, I. U. (2022). Utilizing grid search cross-validation with adaptive boosting for augmenting performance of machine learning models. PeerJ. Computer Science, 8, e803. https://doi.org/10.7717/peerj-cs.803
  • Al-Dhabyani, W., Gomaa, M., Khaled, H., & Fahmy, A. (2020). Dataset of breast ultrasound images. Data in Brief, 28, 104863. https://doi.org/10.1016/j.dib.2019.104863
  • Ali, J., Khan, R., Ahmad, N., & Maqsood, I. (2012). Random forests and decision trees. International Journal of Computer Science Issues, 9(5), 272–278. http://ijcsi.org/papers/IJCSI-9-5-3-272-278.pdf
  • Ara, S., Das, A., & Dey, A. (2021, April). Malignant and benign breast cancer classification using machine learning algorithms. In 2021 International Conference on Artificial Intelligence (ICAI) (pp. 97-101). IEEE.
  • Ardakani, A. A., Bureau, N. J., Ciaccio, E. J., & Acharya, U. R. (2022). Interpretation of radiomics features–A pictorial review. Computer Methods and Programs in Biomedicine, 215, 106609. https://doi.org/10.1016/j.cmpb.2021.106609
  • Aristokli, N., Polycarpou, I., Themistocleous, S. C., Sophocleous, D., & Mamais, I. (2022). Comparison of the diagnostic performance of Magnetic Resonance Imaging (MRI), ultrasound and mammography for detection of breast cancer based on tumor type, breast density and patient's history: A review. Radiography, 28(3), 848–856. https://doi.org/10.1016/j.radi.2022.01.006
  • Assiri, A. S., Nazir, S., & Velastin, S. A. (2020). Breast tumor classification using an ensemble machine learning method. Journal of Imaging, 6(6), 39. https://doi.org/10.3390/jimaging6060039
  • Badawy, S. M., Mohamed, A. E. N. A., Hefnawy, A. A., Zidan, H. E., GadAllah, M. T., & El-Banby, G. M. (2021, July). Classification of Breast Ultrasound Images Based on Convolutional Neural Networks-A Comparative Study. In 2021 International Telecommunications Conference (ITC-Egypt) (pp. 1-8). IEEE.
  • Bota, M. A., Gota, D. I., Bota, P., Stan, O. P., Pop, A., Fanca, A., ... & Miclea, L. (2024, February). Utilizing Transfer Learning-Based Algorithms for Breast Ultrasound Data in Multi-Instance Classification. In 2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) (pp. 1-6). IEEE.
  • CERR. (n.d.). CERR: Computational Environment for Radiotherapy Research. Retrieved September 4, 2024, from https://cerr.github.io/CERR/
  • Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
  • Çapkan, H., Dönmez, B., Kalkan, G. M., Kaya, M. Z., Gürel, S., Akdağlı, E., ... & Uçar, M. K. (2022). Diagnosis of Breast Cancer with Hybrid Artificial Intelligence Method. Avrupa Bilim ve Teknoloji Dergisi, (42), 14-19.
  • Eroğlu, Y., Yildirim, M., & Çinar, A. (2021). Convolutional Neural Networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR. Computers in Biology and Medicine, 133, 104407. https://doi.org/10.1016/j.compbiomed.2021.104407
  • Fornacon-Wood, I., Mistry, H., Ackermann, C. J., Blackhall, F., McPartlin, A., Faivre-Finn, C., ... & O’Connor, J. P. (2020). Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform. European Radiology, 30(12), 6241-6250. https://doi.org/10.1007/s00330-020-06957-9
  • Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5). https://doi.org/10.1214/aos/1013203451
  • Ghabrim, H., Essid, C., & Sakli, H. (2023, February). A diagnostic system for classifying and segmenting breast cancer based on ultrasound images. In 2023 20th International Multi-Conference on Systems, Signals & Devices (SSD) (pp. 919-924). IEEE.
  • Gupta, S., Panwar, A., Yadav, R., Aeri, M., & Manwal, M. (2022, February). Employing Deep Learning Feature Extraction Models with Learning Classifiers to Diagnose Breast Cancer in Medical Images. In 2022 IEEE Delhi Section Conference (DELCON) (pp. 1-6). IEEE.
  • Hussain, Z., Gimenez, F., Yi, D., & Rubin, D. (2017). Differential data augmentation techniques for medical imaging classification tasks. AMIA annual symposium proceedings, 2017, 979-984.
  • Jabeen, K., Khan, M. A., Alhaisoni, M., Tariq, U., Zhang, Y. D., Hamza, A., Khalid, A., Kumar, S., Awais, M., Ali, M., & Damaševičius, R. (2022). Breast cancer classification from ultrasound images using probability-based optimal deep learning feature fusion. Sensors, 22(3), 807. https://doi.org/10.3390/s22030807
  • Khanna, P., Sahu, M., & Singh, B. K. (2021, December). Improving the classification performance of breast ultrasound image using deep learning and optimization algorithm. In 2021 IEEE International Conference on Technology, Research, and Innovation for Betterment of Society (TRIBES) (pp. 1-6). IEEE.
  • Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., Patil, D. C., Little, R. B., Tsien, C. I., & Gillies, R. J. (2012). Radiomics: The process and the challenges. Magnetic Resonance Imaging, 30(9), 1234-1248. https://doi.org/10.1016/j.mri.2012.06.010
  • Lanjewar, M. G., Panchbhai, K. G., & Patle, L. B. (2024). Fusion of transfer learning models with LSTM for detection of breast cancer using ultrasound images. Computers in Biology and Medicine, 169, 107914. https://doi.org/10.1016/j.compbiomed.2023.107914
  • LIFEx. (n.d.). LIFEx: Lesion imaging feature extraction. Retrieved September 4, 2024, from https://www.lifexsoft.org/
  • Liu, H., Cui, G., Luo, Y., Guo, Y., Zhao, L., Wang, Y., . . . Tuncer, T. (2022). Artificial Intelligence-Based breast cancer diagnosis using ultrasound images and Grid-Based Deep Feature Generator. International Journal of General Medicine, Volume 15, 2271–2282. https://doi.org/10.2147/ijgm.s347491
  • Luo, J., Quan, Y., & Xu, S. (2024). Robust-GBDT: GBDT with nonconvex loss for tabular classification in the presence of label noise and class imbalance. arXiv. https://arxiv.org/abs/2310.05067
  • Medical Imaging Interaction Toolkit. (n.d.). The Medical Imaging Interaction Toolkit (MITK). Retrieved September 4, 2024, from https://www.mitk.org/wiki/The_Medical_Imaging_Interaction_Toolkit_(MITK)
  • Mishra, A. K., Roy, P., Bandyopadhyay, S., & Das, S. K. (2021). Breast ultrasound tumour classification: A Machine Learning—Radiomics based approach. Expert Systems, 38(7). https://doi.org/10.1111/exsy.12713
  • Myles, A. J., Feudale, R. N., Liu, Y., Woody, N. A., & Brown, S. D. (2004). An introduction to decision tree modeling. Journal of Chemometrics, 18(6), 275–285. https://doi.org/10.1002/cem.873
  • Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7, 21. https://doi.org/10.3389/fnbot.2013.00021
  • Nielsen, D. (2016). Tree Boosting With XGBoost. Master's thesis. Norwegian University of Science and Technology, Trondheim 98s.
  • Oshiro, T. M., Perez, P. S., & Baranauskas, J. A. (2012). How many trees in a random forest?. In Machine Learning and Data Mining in Pattern Recognition: 8th International Conference, MLDM 2012, Berlin, Germany, July 13-20, 2012. Proceedings 8 (pp. 154-168). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_13
  • Pesapane, F., De Marco, P., Rapino, A., Lombardo, E., Nicosia, L., Tantrige, P., Sardanelli, F., Manfredi, R., Bertelli, E., Castello, R., & Cassano, E. (2023). How radiomics can improve breast cancer diagnosis and treatment. Journal of Clinical Medicine, 12(4), 1372. https://doi.org/10.3390/jcm12041372
  • Pisner, D. A., & Schnyer, D. M. (2020). Support vector machine. In Machine learning (pp. 101-121). Academic Press. https://doi.org/10.1016/B978-0-12-815739-8.00006-7
  • Podda, A. S., Balia, R., Barra, S., Carta, S., Fenu, G., & Piano, L. (2022). Fully-automated deep learning pipeline for segmentation and classification of breast ultrasound images. Journal of Computational Science, 63, 101816. https://doi.org/10.1016/j.jocs.2022.101816
  • Pourasad, Y., Zarouri, E., Parizi, M. S., & Mohammed, A. S. (2021). Presentation of novel architecture for diagnosis and identifying breast cancer location based on ultrasound images using Machine learning. Diagnostics, 11(10), 1870. https://doi.org/10.3390/diagnostics11101870
  • Quinlan, J. R. (1996). Learning decision tree classifiers. ACM Computing Surveys (CSUR), 28(1), 71-72. Rashid, H. U., Ibrikci, T., Paydaş, S., Binokay, F., & Çevik, U. (2022). Analysis of breast cancer classification robustness with radiomics feature extraction and deep learning techniques. Expert Systems, 39(8), e13018. https://doi.org/10.1111/exsy.13018
  • Rizzo, S., Botta, F., Raimondi, S., Origgi, D., Fanciullo, C., Morganti, A. G., & Bellomi, M. (2018). Radiomics: the facts and the challenges of image analysis. European Radiology Experimental, 2(1). https://doi.org/10.1186/s41747-018-0068-z
  • Şenol, A., & Kaya, M. (2024). An investigation on the use of clustering algorithms for data preprocessing in breast cancer diagnosis. Türk Doğa ve Fen Dergisi, 13(1), 70-77. https://doi.org/10.46810/tdfd.1364397 Schapire, R. E. (2013). Explaining AdaBoost. In Springer eBooks (pp. 37–52). https://doi.org/10.1007/978-3-642-41136-6_5
  • Sun, H., Li, H., Si, S., Qi, S., Zhang, W., Ma, H., Chen, X., Liu, Y., Yang, J., Zhang, X., & Qian, W. (2018). Performance evaluation of breast cancer diagnosis with mammography, ultrasonography, and magnetic resonance imaging. Journal of X-ray Science and Technology, 26(5), 805-813. doi: https://doi.org/10.3233/xst-18388
  • Van Griethuysen, J. J., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., . . . Aerts, H. J. (2017). Computational Radiomics system to decode the radiographic phenotype. Cancer Research, 77(21), e104–e107. https://doi.org/10.1158/0008-5472.can-17-0339
  • World Health Organization (WHO). (2024, Eylül 4). Breast cancer. https://www.who.int/news-room/fact-sheets/detail/breast-cancer
  • Zhang, G., Zhao, K., Hong, Y., Qiu, X., Zhang, K., & Wei, B. (2021). SHA-MTL: Soft and hard attention multi-task learning for automated breast cancer ultrasound image segmentation and classification. International Journal of Computer Assisted Radiology and Surgery, 16(12), 1719-1725. https://doi.org/10.1007/s11548-021-02445-7
  • Zhang, W., Guo, Y., & Jin, Q. (2023). Radiomics and its feature selection: A review. Symmetry, 15(10), 1834. https://doi.org/10.3390/sym15101834
  • Zhang, X., Zhang, Y., Zhang, G., Qiu, X., Tan, W., Yin, X., & Liao, L. (2022). Deep learning with radiomics for disease diagnosis and treatment: Challenges and potential. Frontiers in Oncology, 12, 773840. https://doi.org/10.3389/fonc.2022.773840
There are 44 citations in total.

Details

Primary Language Turkish
Subjects Reinforcement Learning, Machine Vision
Journal Section Computer Engineering
Authors

Asuman Kaplan 0009-0004-9357-1773

Esra Kavadar 0009-0005-9548-7548

Mehmet Ali Altuncu 0000-0002-2948-3937

Publication Date March 3, 2025
Submission Date July 8, 2024
Acceptance Date November 7, 2024
Published in Issue Year 2025Volume: 28 Issue: 1

Cite

APA Kaplan, A., Kavadar, E., & Altuncu, M. A. (2025). RADYOMİK ÖZELLİKLER VE MAKİNE ÖĞRENMESİ TEKNİKLERİYLE MEME TÜMÖRLERİNİN SINIFLANDIRILMASI. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 38-50.
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