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DIAGNOSIS OF PROSTATE CANCER WITH ENHANCED EFFICIENCY USING FINE-TUNED CNN AND TRANSFER LEARNING

Year 2024, Volume: 27 Issue: 4, 1306 - 1319, 03.12.2024

Murat Sarıateş , Erdal Özbay

https://doi.org/10.17780/ksujes.1459277

Abstract

Cancer is one of the high-risk diseases for humans. Prostate cases are the second most common disease in men after lung cancer, and early diagnosis is vital. Artificial intelligence technologies have begun to be used in the diagnosis of prostate cancer, and more effective and sensitive results have been obtained, preventing potential errors in human-centered methods. In this study, in order to increase the classification performance in the diagnosis of prostate cancer, transfer learning methods and fine-tuning processes, which have higher success and learning ability with less training data, unlike machine learning methods, were applied. The two-class data set consisting of prostate cancer MR images, ‘significant’ and ‘not-significant’, was classified with Alexnet, Densenet201, Googlenet, and Vgg16 models with the feature extraction approach, and 71.40%, 72.05%, 65%, and 80.13% accuracy results were obtained respectively. To increase these rates, pre-trained transfer learning models were used and accuracy results of 89.74%, 94.32%, 85.59%, and 91.05% were achieved, respectively. A 98.10% validation result was obtained using the cross-validation method in the Densenet201 model. DenseNet201 model achieved the highest accuracy result of 98.63% in transfer learning with the combination of the RMSProp optimization method. The proposed transfer learning model provided an improvement of approximately 26% compared to the feature extraction method.

Keywords

Prostate cancer CNN Deep learning Classification Transfer learning

References

  • Abbasi, A. A., Hussain, L., Awan, I. A., Abbasi, I., Majid, A., Nadeem, M. S. A., Chaudhary, Q. A. (2020). Detecting prostate cancer using deep learning convolution neural network with transfer learning approach. Cognitive Neurodynamics, 14, 523-533.
  • Abdelmaksoud, I. R., Shalaby, A., Mahmoud, A., Elmogy, M., Aboelfetouh, A., Abou El-Ghar, M., El-Baz, A. (2021). Precise identification of prostate cancer from DWI using transfer learning. Sensors, 21(11), 3664.
  • Anguita, D., Ghelardoni, L., Ghio, A., Oneto, L., Ridella, S. (2012, April). The'K'in K-fold Cross Validation. In ESANN (Vol. 102, pp. 441-446).
  • Aslan, M. (2022). Derin Öğrenme Tabanlı Otomatik Beyin Tümör Tespiti. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34(1), 399-407.
  • Bjurlin, M. A., Carroll, P. R., Eggener, S., Fulgham, P. F., Margolis, D. J., Pinto, P. A., ... & Turkbey, B. (2020). Update of the standard operating procedure on the use of multiparametric magnetic resonance imaging for the diagnosis, staging and management of prostate cancer. The Journal of urology, 203(4), 706-712.
  • Carlsson, S., Assel, M., Sjoberg, D., Ulmert, D., Hugosson, J., Lilja, H., & Vickers, A. (2014). Influence of blood prostate specific antigen levels at age 60 on benefits and harms of prostate cancer screening: population based cohort study. Bmj, 348.
  • Chavda, M., & Degadwala, S. (2024). Prostate Cancer Gleason Score Classification Using Transfer Learning Models. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 10(2), 450-458.
  • Dorak, M. T., & Karpuzoglu, E. (2012). Gender differences in cancer susceptibility: an inadequately addressed issue. Frontiers in Genetics, 3, 268.
  • Fırıldak, K., & Talu, M. F. (2019). Evrişimsel sinir ağlarında kullanılan transfer öğrenme yaklaşımlarının incelenmesi. Computer Science, 4(2), 88-95.
  • Geert, L., Oscar, D., Jelle, B., Nico, K., and Henkjan, H.. ProstateX Challenge data, The Cancer Imaging Archive. (2017). https://www.kaggle.com/datasets/tgprostata/transverse-plane-prostate-dataset Accessed 02.05.2024.
  • Hamm, C. A., Baumgärtner, G. L., Biessmann, F., Beetz, N. L., Hartenstein, A., Savic, L. J., Penzkofer, T. (2023). Interactive explainable deep learning model informs prostate cancer diagnosis at MRI. Radiology, 307(4), e222276.
  • Himmerich, H., Kan, C., Au, K., & Treasure, J. (2021). Pharmacological treatment of eating disorders, comorbid mental health problems, malnutrition and physical health consequences. Pharmacology & Therapeutics, 217, 107667.
  • Hoar, D., Lee, P. Q., Guida, A., Patterson, S., Bowen, C. V., Merrimen, J., Clarke, S. E. (2021). Combined transfer learning and test-time augmentation improves convolutional neural network-based semantic segmentation of prostate cancer from multi-parametric MR images. Computer Methods and Programs in Biomedicine, 210, 106375.
  • Kanna, G. P., Kumar, S. J., Parthasarathi, P., & Kumar, Y. (2023). A review on prediction and prognosis of the prostate cancer and gleason grading of prostatic carcinoma using deep transfer learning based approaches. Archives of Computational Methods in Engineering, 30(5), 3113-3132.
  • Kılıçarslan, S., & Pacal, I. (2023). Domates Yapraklarında Hastalık Tespiti İçin Transfer Öğrenme Metotlarının Kullanılması. Mühendislik Bilimleri ve Araştırmaları Dergisi, 5(2), 215-222.
  • Mahesh, T. R., Geman, O., Margala, M., & Guduri, M. (2023). The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification. Healthcare Analytics, 4, 100247.
  • Montesinos López, O. A., Montesinos López, A., & Crossa, J. (2022). Overfitting, model tuning, and evaluation of prediction performance. In Multivariate statistical machine learning methods for genomic prediction (pp. 109-139). Cham: Springer International Publishing.
  • Özbay, E., & Özbay, F. A. (2021). Derin Öğrenme ve Sınıflandırma Yaklaşımları ile BT görüntülerinden Covid-19 Tespiti. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 12(2), 211-219.
  • Özbay, F. A. (2023). A modified seahorse optimization algorithm based on chaotic maps for solving global optimization and engineering problems. Engineering Science and Technology, an International Journal, 41, 101408.
  • Seyyarer, E., Ayata, F., Uçkan, T., & Karci, A. (2020). Derin öğrenmede kullanilan optimizasyon algoritmalarinin uygulanmasi ve kiyaslanmasi. Computer Science, 5(2), 90-98.
  • Srivenkatesh, M. (2020). Prediction of prostate cancer using machine learning algorithms. Int. J. Recent Technol. Eng, 8(5), 5353-5362.
  • Swati, Z. N. K., Zhao, Q., Kabir, M., Ali, F., Ali, Z., Ahmed, S., & Lu, J. (2019). Brain tumor classification for MR images using transfer learning and fine-tuning. Computerized Medical Imaging and Graphics, 75, 34-46.
  • Tsuneki, M., Abe, M., & Kanavati, F. (2022). Transfer learning for adenocarcinoma classifications in the transurethral resection of prostate whole-slide images. Cancers, 14(19), 4744.
  • Weiss, K. R., & Khoshgoftaar, T. M. (2016, November). An investigation of transfer learning and traditional machine learning algorithms. In 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 283-290). IEEE.
  • Yuan, Y., Qin, W., Buyyounouski, M., Ibragimov, B., Hancock, S., Han, B., Xing, L. (2019). Prostate cancer classification with multiparametric MRI transfer learning model. Medical Physics, 46(2), 756-765.
  • Zainudin, Z., Shamsuddin, S. M., & Hasan, S. (2020). Deep layer CNN architecture for breast cancer histopathology image detection. In The International Conference on Advanced Machine Learning Technologies and Applications (Amlta2019) 4 (pp. 43-51). Springer International Publishing.
  • Zhong, X., Cao, R., Shakeri, S., Scalzo, F., Lee, Y., Enzmann, D. R., Sung, K. (2019). Deep transfer learning-based prostate cancer classification using 3 Tesla multi-parametric MRI. Abdominal Radiology, 44, 2030-2039.
  • Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., He, Q. (2020). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), 43-76.

İNCE-AYAR İLE ETKİNLİĞİ ARTIRILMIŞ ESA VE TRANSFER ÖĞRENME YÖNTEMLERİYLE PROSTAT KANSERİNİN TESPİTİ

Year 2024, Volume: 27 Issue: 4, 1306 - 1319, 03.12.2024

Murat Sarıateş , Erdal Özbay

https://doi.org/10.17780/ksujes.1459277

Abstract

Kanser insanlar için yüksek riskli hastalıkların başındadır. Prostat vakaları, akciğer kanserinden sonra erkeklerde ikinci sırada yer almakta ve erken teşhisi hayati önem taşımaktadır. Prostat kanserinin teşhisinde yapay zeka teknolojilerinden faydalanılmaya başlanmış, daha etkili ve hassas sonuçlar elde edilerek insan odaklı yöntemlerdeki potansiyel hatalarının önüne geçilmiştir. Bu çalışmada prostat kanserinin teşhisinde sınıflandırma performansını arttırabilmek adına makine öğrenmesi yöntemlerinden farklı olarak daha az eğitim verisi ile daha yüksek başarı ve öğrenme kabiliyetine sahip transfer öğrenme yöntemi ve ince-ayar işlemleri uygulanmıştır. Prostat kanseri MR görüntülerinden oluşan ‘significant’ ve ‘not-significant’ olmak üzere iki sınıflı veri setine, özellik çıkarımı yaklaşımıyla Alexnet, Densenet201, Googlenet ve Vgg16 modelleriyle sınıflandırılarak sırasıyla %71,40, %72,05, %65,72 ve %80,13 doğruluk sonuçları elde edilmiştir. Bu oranları arttırabilmek adına ön-eğitimli transfer öğrenme modelleri kullanılmış ve sırasıyla %89,74, %94,32, %85,59 ve %91,05 doğruluk sonuçlarına ulaşılmıştır. Densenet201 modelinde çapraz-doğrulama yöntemi kullanılarak %98,10 doğrulama sonucu elde edilmiştir. DenseNet201 modeli transfer öğrenmede RMSProp optimizasyon yöntemi kombinasyonuyla %98,63 ile en yüksek doğruluk sonucuna ulaşmıştır. Önerilen transfer öğrenme modeli, özellik çıkarımı yöntemine kıyasla yaklaşık %26 oranında bir iyileştirme sağlamıştır.

Keywords

Prostat kanseri ESA Derin öğrenme Sınıflandırma Transfer öğrenme

References

  • Abbasi, A. A., Hussain, L., Awan, I. A., Abbasi, I., Majid, A., Nadeem, M. S. A., Chaudhary, Q. A. (2020). Detecting prostate cancer using deep learning convolution neural network with transfer learning approach. Cognitive Neurodynamics, 14, 523-533.
  • Abdelmaksoud, I. R., Shalaby, A., Mahmoud, A., Elmogy, M., Aboelfetouh, A., Abou El-Ghar, M., El-Baz, A. (2021). Precise identification of prostate cancer from DWI using transfer learning. Sensors, 21(11), 3664.
  • Anguita, D., Ghelardoni, L., Ghio, A., Oneto, L., Ridella, S. (2012, April). The'K'in K-fold Cross Validation. In ESANN (Vol. 102, pp. 441-446).
  • Aslan, M. (2022). Derin Öğrenme Tabanlı Otomatik Beyin Tümör Tespiti. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34(1), 399-407.
  • Bjurlin, M. A., Carroll, P. R., Eggener, S., Fulgham, P. F., Margolis, D. J., Pinto, P. A., ... & Turkbey, B. (2020). Update of the standard operating procedure on the use of multiparametric magnetic resonance imaging for the diagnosis, staging and management of prostate cancer. The Journal of urology, 203(4), 706-712.
  • Carlsson, S., Assel, M., Sjoberg, D., Ulmert, D., Hugosson, J., Lilja, H., & Vickers, A. (2014). Influence of blood prostate specific antigen levels at age 60 on benefits and harms of prostate cancer screening: population based cohort study. Bmj, 348.
  • Chavda, M., & Degadwala, S. (2024). Prostate Cancer Gleason Score Classification Using Transfer Learning Models. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 10(2), 450-458.
  • Dorak, M. T., & Karpuzoglu, E. (2012). Gender differences in cancer susceptibility: an inadequately addressed issue. Frontiers in Genetics, 3, 268.
  • Fırıldak, K., & Talu, M. F. (2019). Evrişimsel sinir ağlarında kullanılan transfer öğrenme yaklaşımlarının incelenmesi. Computer Science, 4(2), 88-95.
  • Geert, L., Oscar, D., Jelle, B., Nico, K., and Henkjan, H.. ProstateX Challenge data, The Cancer Imaging Archive. (2017). https://www.kaggle.com/datasets/tgprostata/transverse-plane-prostate-dataset Accessed 02.05.2024.
  • Hamm, C. A., Baumgärtner, G. L., Biessmann, F., Beetz, N. L., Hartenstein, A., Savic, L. J., Penzkofer, T. (2023). Interactive explainable deep learning model informs prostate cancer diagnosis at MRI. Radiology, 307(4), e222276.
  • Himmerich, H., Kan, C., Au, K., & Treasure, J. (2021). Pharmacological treatment of eating disorders, comorbid mental health problems, malnutrition and physical health consequences. Pharmacology & Therapeutics, 217, 107667.
  • Hoar, D., Lee, P. Q., Guida, A., Patterson, S., Bowen, C. V., Merrimen, J., Clarke, S. E. (2021). Combined transfer learning and test-time augmentation improves convolutional neural network-based semantic segmentation of prostate cancer from multi-parametric MR images. Computer Methods and Programs in Biomedicine, 210, 106375.
  • Kanna, G. P., Kumar, S. J., Parthasarathi, P., & Kumar, Y. (2023). A review on prediction and prognosis of the prostate cancer and gleason grading of prostatic carcinoma using deep transfer learning based approaches. Archives of Computational Methods in Engineering, 30(5), 3113-3132.
  • Kılıçarslan, S., & Pacal, I. (2023). Domates Yapraklarında Hastalık Tespiti İçin Transfer Öğrenme Metotlarının Kullanılması. Mühendislik Bilimleri ve Araştırmaları Dergisi, 5(2), 215-222.
  • Mahesh, T. R., Geman, O., Margala, M., & Guduri, M. (2023). The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification. Healthcare Analytics, 4, 100247.
  • Montesinos López, O. A., Montesinos López, A., & Crossa, J. (2022). Overfitting, model tuning, and evaluation of prediction performance. In Multivariate statistical machine learning methods for genomic prediction (pp. 109-139). Cham: Springer International Publishing.
  • Özbay, E., & Özbay, F. A. (2021). Derin Öğrenme ve Sınıflandırma Yaklaşımları ile BT görüntülerinden Covid-19 Tespiti. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 12(2), 211-219.
  • Özbay, F. A. (2023). A modified seahorse optimization algorithm based on chaotic maps for solving global optimization and engineering problems. Engineering Science and Technology, an International Journal, 41, 101408.
  • Seyyarer, E., Ayata, F., Uçkan, T., & Karci, A. (2020). Derin öğrenmede kullanilan optimizasyon algoritmalarinin uygulanmasi ve kiyaslanmasi. Computer Science, 5(2), 90-98.
  • Srivenkatesh, M. (2020). Prediction of prostate cancer using machine learning algorithms. Int. J. Recent Technol. Eng, 8(5), 5353-5362.
  • Swati, Z. N. K., Zhao, Q., Kabir, M., Ali, F., Ali, Z., Ahmed, S., & Lu, J. (2019). Brain tumor classification for MR images using transfer learning and fine-tuning. Computerized Medical Imaging and Graphics, 75, 34-46.
  • Tsuneki, M., Abe, M., & Kanavati, F. (2022). Transfer learning for adenocarcinoma classifications in the transurethral resection of prostate whole-slide images. Cancers, 14(19), 4744.
  • Weiss, K. R., & Khoshgoftaar, T. M. (2016, November). An investigation of transfer learning and traditional machine learning algorithms. In 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 283-290). IEEE.
  • Yuan, Y., Qin, W., Buyyounouski, M., Ibragimov, B., Hancock, S., Han, B., Xing, L. (2019). Prostate cancer classification with multiparametric MRI transfer learning model. Medical Physics, 46(2), 756-765.
  • Zainudin, Z., Shamsuddin, S. M., & Hasan, S. (2020). Deep layer CNN architecture for breast cancer histopathology image detection. In The International Conference on Advanced Machine Learning Technologies and Applications (Amlta2019) 4 (pp. 43-51). Springer International Publishing.
  • Zhong, X., Cao, R., Shakeri, S., Scalzo, F., Lee, Y., Enzmann, D. R., Sung, K. (2019). Deep transfer learning-based prostate cancer classification using 3 Tesla multi-parametric MRI. Abdominal Radiology, 44, 2030-2039.
  • Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., He, Q. (2020). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), 43-76.
There are 28 citations in total.

Details

Primary Language English
Subjects Deep Learning
Journal Section Computer Engineering
Authors

Murat Sarıateş 0009-0003-4351-9566

Erdal Özbay 0000-0002-9004-4802

Publication Date December 3, 2024
Submission Date March 26, 2024
Acceptance Date May 6, 2024
Published in Issue Year 2024Volume: 27 Issue: 4

Cite

APA Sarıateş, M., & Özbay, E. (2024). DIAGNOSIS OF PROSTATE CANCER WITH ENHANCED EFFICIENCY USING FINE-TUNED CNN AND TRANSFER LEARNING. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(4), 1306-1319. https://doi.org/10.17780/ksujes.1459277
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