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Advanced CNN-Based Classification and Segmentation for Enhanced Breast Cancer Ultrasound Imaging

Yıl 2024, Cilt: 11 Sayı: 4, 647 - 667, 30.12.2024

Jehad Cheyi , Yasemin Çetin Kaya

https://doi.org/10.54287/gujsa.1529857
Cited By: 1

Öz

Breast cancer (BC) is one of the primary causes of mortality in women globally. Thus, early and exact identification is critical for effective treatment. This work investigates deep learning, more especially convolutional neural networks (CNNs), to classify BC from ultrasound images. We worked with a collection of breast ultrasound images from 600 patients. Our approach included extensive image preprocessing techniques, such as enhancement and overlay methods, before training various deep learning models with particular reference to VGG16, VGG19, ResNet50, DenseNet121, EfficientNetB0, and custom CNNs. Our proposed model achieved a remarkable classification accuracy of 97%, significantly outperforming established models like EfficientNetB0, MobileNet, and Inceptionv3. This research demonstrates the ability of advanced CNNs, when paired with good preprocessing, to significantly enhance BC classification from ultrasound images. We further used Grad-CAM to make the model interpretable so we may see which parts of the images the CNNs focus on when making decisions.

Anahtar Kelimeler

Deep Learning CNN Breast Cancer Classification Image Processing

Kaynakça

  • 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
  • Alrubaie, H., Aljobouri, H. K., AL-Jobawi, Z. J., & Çankaya, I. (2023). Convolutional neural network deep learning model for improved ultrasound breast tumor classification. Al-Nahrain Journal for Engineering Sciences, 26(2), 57-62. https://doi.org/10.29194/NJES.26020057
  • Badawy, S. M., Mohamed, A. E.-N. A., Hefnawy, A. A., Zidan, H. E., GadAllah, M. T., & El-Banby, G. M. (2021). Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning: A feasibility study. PLOS ONE, 16(5), e0251899. https://doi.org/10.1371/journal.pone.0251899
  • Cao, Z., Yang, G., Chen, Q., Chen, X., & Lv, F. (2020). Breast tumor classification through learning from noisy labeled ultrasound images. Medical Physics, 47(3), 1048-1057. https://doi.org/10.1002/mp.13966
  • Chiang, T.-C., Huang, Y.-S., Chen, R.-T., Huang, C.-S., & Chang, R.-F. (2018). Tumor detection in automated breast ultrasound using 3-D CNN and prioritized candidate aggregation. IEEE Transactions on Medical Imaging, 38(1), 240-249. https://doi.org/10.1109/TMI.2018.2860257
  • Coronado-Gutiérrez, D., Santamaría, G., Ganau, S., Bargalló, X., Orlando, S., Oliva-Brañas, M. E., Perez-Moreno, A., & Burgos-Artizzu, X. P. (2019). Quantitative ultrasound image analysis of axillary lymph nodes to diagnose metastatic involvement in breast cancer. Ultrasound in Medicine & Biology, 45(11), 2932-2941. https://doi.org/10.1016/j.ultrasmedbio.2019.07.413
  • Cruz-Ramos, C., García-Ávila, O., Almaraz-Damián, J.-A., Ponomaryov, V., Reyes-Reyes, R., & Sadovnychiy, S. (2023). Benign and malignant breast tumor classification in ultrasound and mammography images via fusion of deep learning and handcraft features. Entropy, 25(7), 991. https://doi.org/10.3390/e25070991
  • Çetin-Kaya, Y., & Kaya, M. (2024). A novel ensemble framework for multi-classification of brain tumors using magnetic resonance imaging. Diagnostics, 14(4), 383. https://doi.org/10.3390/diagnostics14040383
  • Fujioka, T., Mori, M., Kubota, K., Oyama, J., Yamaga, E., Yashima, Y., Katsuta, L., Nomura, K., Nara, M., Oda, G., Nakagawa, T., Kitazume, Y., & Tateishi, U. (2020). The utility of deep learning in breast ultrasonic imaging: A review. Diagnostics, 10(12), 1055. https://doi.org/10.3390/diagnostics10121055
  • Gómez-Flores, W., & de Albuquerque Pereira, W. C. (2020). A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound. Computers in Biology and Medicine, 126, 104036. https://doi.org/10.1016/j.compbiomed.2020.104036
  • Gong, B., Shen, L., Chang, C., Zhou, S., Zhou, W., Li, S., & Shi, J. (2020, April 3-7). BI-modal ultrasound breast cancer diagnosis via multi-view deep neural network SVM. In: Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (pp. 1106-1110), Iowa City, IA, USA. IEEE. https://doi.org/10.1109/ISBI45749.2020.9098438
  • Han, S., Kang, H.-K., Jeong, J.-Y., Park, M.-H., Kim, W., Bang, W.-C., & Seong, Y.-K. (2017). A deep learning framework for supporting the classification of breast lesions in ultrasound images. Physics in Medicine & Biology, 62(19), 7714. https://doi.org/10.1088/1361-6560/aa82ec
  • Ilesanmi, A. E., Chaumrattanakul, U., & Makhanov, S. S. (2021). A method for segmentation of tumors in breast ultrasound images using the variant enhanced deep learning. Biocybernetics and Biomedical Engineering, 41(2), 802-818. https://doi.org/10.1016/j.bbe.2021.05.007
  • Jabeen, K., Khan, M. A., Alhaisoni, M., Tariq, U., Zhang, Y.-D., Hamza, A., Mickus, A., & 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
  • Kabir, S. M., Bhuiyan, M. I. H., Tanveer, M. S., & Shihavuddin, ASM. (2021). RiIG modeled WCP image-based CNN architecture and feature-based approach in breast tumor classification from B-mode ultrasound. Applied Sciences, 11(24), 12138. https://doi.org/10.3390/app112412138
  • Kaya, M., & Çetin-Kaya, Y. (2024). A novel ensemble learning framework based on a genetic algorithm for the classification of pneumonia. Engineering Applications of Artificial Intelligence, 133, 108494. https://doi.org/10.1016/j.engappai.2024.108494
  • Kim, J., Kim, H. J., Kim, C., Lee, J. H., Kim, K. W., Park, Y. M., Kim, H. W., Ki, S. Y., Kim, Y. M., & Kim, W. H. (2021). Weakly supervised deep learning for ultrasound diagnosis of breast cancer. Scientific Reports, 11(1), 24382. https://doi.org/10.1038/s41598-021-03806-7
  • Lei, B., Huang, S., Li, R., Bian, C., Li, H., Chou, Y.-H., & Cheng, J.-Z. (2018). Segmentation of breast anatomy for automated whole breast ultrasound images with boundary regularized convolutional encoder-decoder network. Neurocomputing, 321, 178-186. https://doi.org/10.1016/j.neucom.2018.09.043
  • Li, Y., Gu, H., Wang, H., Qin, P., & Wang, J. (2022). BUSnet: A deep learning model of breast tumor lesion detection for ultrasound images. Frontiers in Oncology, 12, 848271. https://doi.org/10.3389/fonc.2022.848271
  • Liu, H., Cui, G., Luo, Y., Guo, Y., Zhao, L., Wang, Y., Subasi, A., Dogan, S., & Tuncer, T. (2022). Artificial intelligence-based breast cancer diagnosis using ultrasound images and grid-based deep feature generator. International Journal of General Medicine, 15, 2271-2282. https://doi.org/10.2147/IJGM.S347491
  • Luo, L., Wang, X., Lin, Y., Ma, X., Tan, A., Chan, R., Vardhanabhuti, V., Chu, W. C., Cheng, K.-T., & Chen, H. (2024). Deep learning in breast cancer imaging: A decade of progress and future directions. IEEE Reviews in Biomedical Engineering. https://doi.org/10.1109/RBME.2024.3357877
  • Mahoro, E., & Akhloufi, M. A. (2022). Applying deep learning for breast cancer detection in radiology. Current Oncology, 29(11), 8767-8793. https://doi.org/10.3390/curroncol29110690
  • Marini, T. J., Castaneda, B., Iyer, R., Baran, T. M., Nemer, O., Dozier, A. M., Parker, K. J., Zhao, Y., Serratelli, W., Matos, G., Ali, S., Ghobryal, B., Visca, A., & O’Connell, A. (2023). Breast ultrasound volume sweep imaging: A new horizon in expanding imaging access for breast cancer detection. Journal of Ultrasound in Medicine, 42(4), 817-832. https://doi.org/10.1002/jum.16047
  • Masud, M., Hossain, M. S., Alhumyani, H., Alshamrani, S. S., Cheikhrouhou, O., Ibrahim, S., Muhammad, G., Rashed, A. E. E., & Gupta, B. B. (2021). Pre-trained convolutional neural networks for breast cancer detection using ultrasound images. ACM Transactions on Internet Technology, 21(4), 1-17. https://doi.org/10.1145/3418355
  • Momot, A., Galagan, R., & Zaboluieva, M. (2022). Automation of ultrasound breast cancer image classification using deep neural networks. Sciences of Europe, (96), 38-41.
  • Moon, W. K., Lee, Y.-W., Ke, H.-H., Lee, S. H., Huang, C.-S., & Chang, R.-F. (2020). Computer‐aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks. Computer Methods and Programs in Biomedicine, 190, 105361. https://doi.org/10.1016/j.cmpb.2020.105361
  • Moustafa, A. F., Cary, T. W., Sultan, L. R., Schultz, S. M., Conant, E. F., Venkatesh, S. S., & Sehgal, C. M. (2020). Color Doppler ultrasound improves machine learning diagnosis of breast cancer. Diagnostics, 10(9), 631. https://doi.org/10.3390/diagnostics10090631
  • Negi, A., Raj, A. N. J., Nersisson, R., Zhuang, Z., & Murugappan, M. (2020). RDA-UNET-WGAN: An accurate breast ultrasound lesion segmentation using Wasserstein generative adversarial networks. Arabian Journal for Science and Engineering, 45(8), 6399-6410. https://doi.org/10.1007/s13369-020-04480-z
  • Pacal, İ. (2022). Deep learning approaches for classification of breast cancer in ultrasound (US) images. Journal of the Institute of Science and Technology, 12(4), 1917-1927. https://doi.org/10.21597/jist.1183679
  • Pang, T., Wong, J. H. D., Ng, W. L., & Chan, C. S. (2021). Semi-supervised GAN-based radiomics model for data augmentation in breast ultrasound mass classification. Computer Methods and Programs in Biomedicine, 203, 106018. https://doi.org/10.1016/j.cmpb.2021.106018
  • Peng, Y., Tang, W., & Peng, X. (2023). The study of ultrasonography based on deep learning in breast cancer. Journal of Radiation Research and Applied Sciences, 16(4), 100679. https://doi.org/10.1016/j.jrras.2023.100679
  • Pourasad, Y., Zarouri, E., Salemizadeh Parizi, M., & Salih Mohammed, A. (2021). Presentation of a 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
  • Qi, X., Zhang, L., Chen, Y., Pi, Y., Chen, Y., Lv, Q., & Yi, Z. (2019). Automated diagnosis of breast ultrasonography images using deep neural networks. Medical Image Analysis, 52, 185-198. https://doi.org/10.1016/j.media.2018.12.006
  • Vakanski, A., Xian, M., & Freer, P. E. (2020). Attention-enriched deep learning model for breast tumor segmentation in ultrasound images. Ultrasound in Medicine & Biology, 46(10), 2819-2833. https://doi.org/10.1016/j.ultrasmedbio.2020.06.015
  • Vigil, N., Barry, M., Amini, A., Akhloufi, M., Maldague, X. P. V., Ma, L., Ren, L., & Yousefi, B. (2022). Dual-intended deep learning model for breast cancer diagnosis in ultrasound imaging. Cancers, 14(11), 2663. https://doi.org/10.3390/cancers14112663
  • Wu, G. G., Zhou, L.-Q., Xu, J.-W., Wang, J.-Y., Wei, Q., Deng, Y.-B., Cui, X.-W., & Dietrich, C. F. (2019). Artificial intelligence in breast ultrasound. World Journal of Radiology, 11(2), 19-26. https://doi.org/10.4329/wjr.v11.i2.19
  • Wu, T., Sultan, L. R., Tian, J., Cary, T. W., & Sehgal, C. M. (2019). Machine learning for diagnostic ultrasound of triple-negative breast cancer. Breast Cancer Research and Treatment, 173(2), 365-373. https://doi.org/10.1007/s10549-018-4984-7
  • Xu, Y., Wang, Y., Yuan, J., Cheng, Q., Wang, X., & Carson, P. L. (2019). Medical breast ultrasound image segmentation by machine learning. Ultrasonics, 91, 1-9. https://doi.org/10.1016/j.ultras.2018.07.006
  • Zhang, E., Seiler, S., Chen, M., Lu, W., & Gu, X. (2019, July 23-27). Boundary-aware semi-supervised deep learning for breast ultrasound computer-aided diagnosis. In: Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 947-950), Berlin, Germany. IEEE. https://doi.org/10.1109/EMBC.2019.8856539
  • Zhang, Y., Chen, J.-H., Lin, Y., Chan, S., Zhou, J., Chow, D., Chang, P., Kwong, T., Yeh, D.-C., Wang, X., Parajuli, R., Mehta, R. S., Wang, M., & Su, M.-Y. (2021). Prediction of breast cancer molecular subtypes on DCE-MRI using a convolutional neural network with transfer learning between two centers. European Radiology, 31(4), 2559-2567. https://doi.org/10.1007/s00330-020-07274-x
  • Zhang, Z., Li, Y., Wu, W., Chen, H., Cheng, L., & Wang, S. (2021). Tumor detection using deep learning method in automated breast ultrasound. Biomedical Signal Processing and Control, 68, 102677. https://doi.org/10.1016/j.bspc.2021.102677
  • Zhuang, Z., Yang, Z., Raj, A. N. J., Wei, C., Jin, P., & Zhuang, S. (2021). Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi-model spatial feature fusion. Computer Methods and Programs in Biomedicine, 208, 106221. https://doi.org/10.1016/j.cmpb.2021.106221

Yıl 2024, Cilt: 11 Sayı: 4, 647 - 667, 30.12.2024

Jehad Cheyi , Yasemin Çetin Kaya

https://doi.org/10.54287/gujsa.1529857
Cited By: 1

Öz

Kaynakça

  • 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
  • Alrubaie, H., Aljobouri, H. K., AL-Jobawi, Z. J., & Çankaya, I. (2023). Convolutional neural network deep learning model for improved ultrasound breast tumor classification. Al-Nahrain Journal for Engineering Sciences, 26(2), 57-62. https://doi.org/10.29194/NJES.26020057
  • Badawy, S. M., Mohamed, A. E.-N. A., Hefnawy, A. A., Zidan, H. E., GadAllah, M. T., & El-Banby, G. M. (2021). Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning: A feasibility study. PLOS ONE, 16(5), e0251899. https://doi.org/10.1371/journal.pone.0251899
  • Cao, Z., Yang, G., Chen, Q., Chen, X., & Lv, F. (2020). Breast tumor classification through learning from noisy labeled ultrasound images. Medical Physics, 47(3), 1048-1057. https://doi.org/10.1002/mp.13966
  • Chiang, T.-C., Huang, Y.-S., Chen, R.-T., Huang, C.-S., & Chang, R.-F. (2018). Tumor detection in automated breast ultrasound using 3-D CNN and prioritized candidate aggregation. IEEE Transactions on Medical Imaging, 38(1), 240-249. https://doi.org/10.1109/TMI.2018.2860257
  • Coronado-Gutiérrez, D., Santamaría, G., Ganau, S., Bargalló, X., Orlando, S., Oliva-Brañas, M. E., Perez-Moreno, A., & Burgos-Artizzu, X. P. (2019). Quantitative ultrasound image analysis of axillary lymph nodes to diagnose metastatic involvement in breast cancer. Ultrasound in Medicine & Biology, 45(11), 2932-2941. https://doi.org/10.1016/j.ultrasmedbio.2019.07.413
  • Cruz-Ramos, C., García-Ávila, O., Almaraz-Damián, J.-A., Ponomaryov, V., Reyes-Reyes, R., & Sadovnychiy, S. (2023). Benign and malignant breast tumor classification in ultrasound and mammography images via fusion of deep learning and handcraft features. Entropy, 25(7), 991. https://doi.org/10.3390/e25070991
  • Çetin-Kaya, Y., & Kaya, M. (2024). A novel ensemble framework for multi-classification of brain tumors using magnetic resonance imaging. Diagnostics, 14(4), 383. https://doi.org/10.3390/diagnostics14040383
  • Fujioka, T., Mori, M., Kubota, K., Oyama, J., Yamaga, E., Yashima, Y., Katsuta, L., Nomura, K., Nara, M., Oda, G., Nakagawa, T., Kitazume, Y., & Tateishi, U. (2020). The utility of deep learning in breast ultrasonic imaging: A review. Diagnostics, 10(12), 1055. https://doi.org/10.3390/diagnostics10121055
  • Gómez-Flores, W., & de Albuquerque Pereira, W. C. (2020). A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound. Computers in Biology and Medicine, 126, 104036. https://doi.org/10.1016/j.compbiomed.2020.104036
  • Gong, B., Shen, L., Chang, C., Zhou, S., Zhou, W., Li, S., & Shi, J. (2020, April 3-7). BI-modal ultrasound breast cancer diagnosis via multi-view deep neural network SVM. In: Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (pp. 1106-1110), Iowa City, IA, USA. IEEE. https://doi.org/10.1109/ISBI45749.2020.9098438
  • Han, S., Kang, H.-K., Jeong, J.-Y., Park, M.-H., Kim, W., Bang, W.-C., & Seong, Y.-K. (2017). A deep learning framework for supporting the classification of breast lesions in ultrasound images. Physics in Medicine & Biology, 62(19), 7714. https://doi.org/10.1088/1361-6560/aa82ec
  • Ilesanmi, A. E., Chaumrattanakul, U., & Makhanov, S. S. (2021). A method for segmentation of tumors in breast ultrasound images using the variant enhanced deep learning. Biocybernetics and Biomedical Engineering, 41(2), 802-818. https://doi.org/10.1016/j.bbe.2021.05.007
  • Jabeen, K., Khan, M. A., Alhaisoni, M., Tariq, U., Zhang, Y.-D., Hamza, A., Mickus, A., & 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
  • Kabir, S. M., Bhuiyan, M. I. H., Tanveer, M. S., & Shihavuddin, ASM. (2021). RiIG modeled WCP image-based CNN architecture and feature-based approach in breast tumor classification from B-mode ultrasound. Applied Sciences, 11(24), 12138. https://doi.org/10.3390/app112412138
  • Kaya, M., & Çetin-Kaya, Y. (2024). A novel ensemble learning framework based on a genetic algorithm for the classification of pneumonia. Engineering Applications of Artificial Intelligence, 133, 108494. https://doi.org/10.1016/j.engappai.2024.108494
  • Kim, J., Kim, H. J., Kim, C., Lee, J. H., Kim, K. W., Park, Y. M., Kim, H. W., Ki, S. Y., Kim, Y. M., & Kim, W. H. (2021). Weakly supervised deep learning for ultrasound diagnosis of breast cancer. Scientific Reports, 11(1), 24382. https://doi.org/10.1038/s41598-021-03806-7
  • Lei, B., Huang, S., Li, R., Bian, C., Li, H., Chou, Y.-H., & Cheng, J.-Z. (2018). Segmentation of breast anatomy for automated whole breast ultrasound images with boundary regularized convolutional encoder-decoder network. Neurocomputing, 321, 178-186. https://doi.org/10.1016/j.neucom.2018.09.043
  • Li, Y., Gu, H., Wang, H., Qin, P., & Wang, J. (2022). BUSnet: A deep learning model of breast tumor lesion detection for ultrasound images. Frontiers in Oncology, 12, 848271. https://doi.org/10.3389/fonc.2022.848271
  • Liu, H., Cui, G., Luo, Y., Guo, Y., Zhao, L., Wang, Y., Subasi, A., Dogan, S., & Tuncer, T. (2022). Artificial intelligence-based breast cancer diagnosis using ultrasound images and grid-based deep feature generator. International Journal of General Medicine, 15, 2271-2282. https://doi.org/10.2147/IJGM.S347491
  • Luo, L., Wang, X., Lin, Y., Ma, X., Tan, A., Chan, R., Vardhanabhuti, V., Chu, W. C., Cheng, K.-T., & Chen, H. (2024). Deep learning in breast cancer imaging: A decade of progress and future directions. IEEE Reviews in Biomedical Engineering. https://doi.org/10.1109/RBME.2024.3357877
  • Mahoro, E., & Akhloufi, M. A. (2022). Applying deep learning for breast cancer detection in radiology. Current Oncology, 29(11), 8767-8793. https://doi.org/10.3390/curroncol29110690
  • Marini, T. J., Castaneda, B., Iyer, R., Baran, T. M., Nemer, O., Dozier, A. M., Parker, K. J., Zhao, Y., Serratelli, W., Matos, G., Ali, S., Ghobryal, B., Visca, A., & O’Connell, A. (2023). Breast ultrasound volume sweep imaging: A new horizon in expanding imaging access for breast cancer detection. Journal of Ultrasound in Medicine, 42(4), 817-832. https://doi.org/10.1002/jum.16047
  • Masud, M., Hossain, M. S., Alhumyani, H., Alshamrani, S. S., Cheikhrouhou, O., Ibrahim, S., Muhammad, G., Rashed, A. E. E., & Gupta, B. B. (2021). Pre-trained convolutional neural networks for breast cancer detection using ultrasound images. ACM Transactions on Internet Technology, 21(4), 1-17. https://doi.org/10.1145/3418355
  • Momot, A., Galagan, R., & Zaboluieva, M. (2022). Automation of ultrasound breast cancer image classification using deep neural networks. Sciences of Europe, (96), 38-41.
  • Moon, W. K., Lee, Y.-W., Ke, H.-H., Lee, S. H., Huang, C.-S., & Chang, R.-F. (2020). Computer‐aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks. Computer Methods and Programs in Biomedicine, 190, 105361. https://doi.org/10.1016/j.cmpb.2020.105361
  • Moustafa, A. F., Cary, T. W., Sultan, L. R., Schultz, S. M., Conant, E. F., Venkatesh, S. S., & Sehgal, C. M. (2020). Color Doppler ultrasound improves machine learning diagnosis of breast cancer. Diagnostics, 10(9), 631. https://doi.org/10.3390/diagnostics10090631
  • Negi, A., Raj, A. N. J., Nersisson, R., Zhuang, Z., & Murugappan, M. (2020). RDA-UNET-WGAN: An accurate breast ultrasound lesion segmentation using Wasserstein generative adversarial networks. Arabian Journal for Science and Engineering, 45(8), 6399-6410. https://doi.org/10.1007/s13369-020-04480-z
  • Pacal, İ. (2022). Deep learning approaches for classification of breast cancer in ultrasound (US) images. Journal of the Institute of Science and Technology, 12(4), 1917-1927. https://doi.org/10.21597/jist.1183679
  • Pang, T., Wong, J. H. D., Ng, W. L., & Chan, C. S. (2021). Semi-supervised GAN-based radiomics model for data augmentation in breast ultrasound mass classification. Computer Methods and Programs in Biomedicine, 203, 106018. https://doi.org/10.1016/j.cmpb.2021.106018
  • Peng, Y., Tang, W., & Peng, X. (2023). The study of ultrasonography based on deep learning in breast cancer. Journal of Radiation Research and Applied Sciences, 16(4), 100679. https://doi.org/10.1016/j.jrras.2023.100679
  • Pourasad, Y., Zarouri, E., Salemizadeh Parizi, M., & Salih Mohammed, A. (2021). Presentation of a 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
  • Qi, X., Zhang, L., Chen, Y., Pi, Y., Chen, Y., Lv, Q., & Yi, Z. (2019). Automated diagnosis of breast ultrasonography images using deep neural networks. Medical Image Analysis, 52, 185-198. https://doi.org/10.1016/j.media.2018.12.006
  • Vakanski, A., Xian, M., & Freer, P. E. (2020). Attention-enriched deep learning model for breast tumor segmentation in ultrasound images. Ultrasound in Medicine & Biology, 46(10), 2819-2833. https://doi.org/10.1016/j.ultrasmedbio.2020.06.015
  • Vigil, N., Barry, M., Amini, A., Akhloufi, M., Maldague, X. P. V., Ma, L., Ren, L., & Yousefi, B. (2022). Dual-intended deep learning model for breast cancer diagnosis in ultrasound imaging. Cancers, 14(11), 2663. https://doi.org/10.3390/cancers14112663
  • Wu, G. G., Zhou, L.-Q., Xu, J.-W., Wang, J.-Y., Wei, Q., Deng, Y.-B., Cui, X.-W., & Dietrich, C. F. (2019). Artificial intelligence in breast ultrasound. World Journal of Radiology, 11(2), 19-26. https://doi.org/10.4329/wjr.v11.i2.19
  • Wu, T., Sultan, L. R., Tian, J., Cary, T. W., & Sehgal, C. M. (2019). Machine learning for diagnostic ultrasound of triple-negative breast cancer. Breast Cancer Research and Treatment, 173(2), 365-373. https://doi.org/10.1007/s10549-018-4984-7
  • Xu, Y., Wang, Y., Yuan, J., Cheng, Q., Wang, X., & Carson, P. L. (2019). Medical breast ultrasound image segmentation by machine learning. Ultrasonics, 91, 1-9. https://doi.org/10.1016/j.ultras.2018.07.006
  • Zhang, E., Seiler, S., Chen, M., Lu, W., & Gu, X. (2019, July 23-27). Boundary-aware semi-supervised deep learning for breast ultrasound computer-aided diagnosis. In: Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 947-950), Berlin, Germany. IEEE. https://doi.org/10.1109/EMBC.2019.8856539
  • Zhang, Y., Chen, J.-H., Lin, Y., Chan, S., Zhou, J., Chow, D., Chang, P., Kwong, T., Yeh, D.-C., Wang, X., Parajuli, R., Mehta, R. S., Wang, M., & Su, M.-Y. (2021). Prediction of breast cancer molecular subtypes on DCE-MRI using a convolutional neural network with transfer learning between two centers. European Radiology, 31(4), 2559-2567. https://doi.org/10.1007/s00330-020-07274-x
  • Zhang, Z., Li, Y., Wu, W., Chen, H., Cheng, L., & Wang, S. (2021). Tumor detection using deep learning method in automated breast ultrasound. Biomedical Signal Processing and Control, 68, 102677. https://doi.org/10.1016/j.bspc.2021.102677
  • Zhuang, Z., Yang, Z., Raj, A. N. J., Wei, C., Jin, P., & Zhuang, S. (2021). Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi-model spatial feature fusion. Computer Methods and Programs in Biomedicine, 208, 106221. https://doi.org/10.1016/j.cmpb.2021.106221
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Derin Öğrenme
Bölüm Bilgi ve Bilgi İşleme Bilimleri
Yazarlar

Jehad Cheyi 0009-0003-5407-569X

Yasemin Çetin Kaya 0000-0002-6745-7705

Yayımlanma Tarihi 30 Aralık 2024
Gönderilme Tarihi 8 Ağustos 2024
Kabul Tarihi 7 Ekim 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 11 Sayı: 4

Kaynak Göster

APA Cheyi, J., & Çetin Kaya, Y. (2024). Advanced CNN-Based Classification and Segmentation for Enhanced Breast Cancer Ultrasound Imaging. Gazi University Journal of Science Part A: Engineering and Innovation, 11(4), 647-667. https://doi.org/10.54287/gujsa.1529857

Cited By

Efficient Diagnosis of Retinal Diseases Using Convolutional Neural Networks
Gazi University Journal of Science Part A: Engineering and Innovation
https://doi.org/10.54287/gujsa.1592915
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