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Enhancing Early Breast Cancer Diagnosis with MRMR and GRU-based Model

Yıl 2024, Cilt: 24 Sayı: 2, 320 - 329, 29.04.2024

Samet Aymaz

https://doi.org/10.35414/akufemubid.1360156

Öz

Breast cancer is one of the most common cancers in women worldwide and early detection can be life-saving. This study aims to develop an accurate and reliable model for breast cancer diagnosis by focusing on the Wisconsin Breast Cancer Diagnosis (WDBC) dataset. In the first stage, feature selection was performed using the Minimum Redundancy Maximum Relevance (MRMR) method. The method is used as an effective tool in the field of data mining and feature selection. With MRMR, the importance of the features is ranked and only the significant ones are used. Feature selection improves performance while reducing the complexity of the model. Then, these features selected by MRMR are classified by a Gated Recurrent Unit (GRU) based neural network model for breast cancer classification. The GRU is capable of handling one-dimensional feature series and gives effective results in complex classification problems. The results show that this innovative approach is highly successful in breast cancer diagnosis. In the evaluations, 98.28% for accuracy metric, 98.59% for precision metric, 98.59% for sensitivity metric, 97.67% for specificity metric and 98.59% for f-score metric were obtained. The results show that the method can help specialists in clinical practice. It is understood from the results that the proposed approach has important advantages such as accessibility for all segments of the society, fast and high accuracy even in simple systems.

Anahtar Kelimeler

Breast cancer MRMR method GRU model Feature selection

Kaynakça

  • Agarap, A. F. (2018). Deep learning using rectified linear units (relu). ArXiv Preprint ArXiv:1803.08375.
  • Ahmed, Y. A., Koçer, B., Huda, S., Al-Rimy, B. a. S., & Hassan, M. M. (2020). A system call refinement-based enhanced Minimum Redundancy Maximum Relevance method for ransomware early detection. Journal of Network and Computer Applications, 167, 102753. https://doi.org/10.1016/j.jnca.2020.102753
  • Albadr, M. A. A., Ayob, M., Tiun, S., AL-Dhief, F. T., Arram, A., & Khalaf, S. (2023, April 27). Breast cancer diagnosis using the fast learning network algorithm. Frontiers in Oncology, 13. https://doi.org/10.3389/fonc.2023.1150840
  • Alshayeji, M. H., Ellethy, H., Abed, S., & Gupta, R. (2022, January). Computer-aided detection of breast cancer on the Wisconsin dataset: An artificial neural networks approach. Biomedical Signal Processing and Control, 71, 103141. https://doi.org/10.1016/j.bspc.2021.103141
  • Aswathy, M., & Mani, M. P. (2017). Detection of breast cancer on digital histopathology images: Present status and future possibilities. Informatics in Medicine Unlocked, 8, 74–79. https://doi.org/10.1016/j.imu.2016.11.001
  • Bhardwaj, A., Bhardwaj, H., Sakalle, A., Uddin, Z., Sakalle, M., & Ibrahim, W. (2022). Tree-Based and Machine learning algorithm analysis for breast cancer classification. Computational Intelligence and Neuroscience, 2022, 1–6. https://doi.org/10.1155/2022/6715406
  • Billah, M., & Waheed, S. (2020). Minimum redundancy maximum relevance (mRMR) based feature selection from endoscopic images for automatic gastrointestinal polyp detection. Multimedia Tools and Applications, 79(33–34), 23633–23643. https://doi.org/10.1007/s11042-020-09151-7
  • Chen, J., Jing, H., Chang, Y., & Liu, Q. (2019). Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process. Reliability Engineering & System Safety, 185, 372–382. https://doi.org/10.1016/j.ress.2019.01.006
  • Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1406.1078
  • Cho, K., Van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259.
  • Ding, C., and H. Peng. "Minimum redundancy feature selection from microarray gene expression data." Journal of Bioinformatics and Computational Biology. Vol. 3, Number 2, 2005, pp. 185-205.
  • Freund, Y., Schapire, R.E. (1995). A desicion-theoretic generalization of on-line learning and an application to boosting. In: Vitânyi, P. (eds) Computational Learning Theory. EuroCOLT 1995. Lecture Notes in Computer Science, vol 904. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59119-2_166
  • Goutte, C., & Gaussier, E. (2005). A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation. In Springer eBooks (pp. 345–359). https://doi.org/10.1007/978-3-540-31865-1_25
  • Goodfellow, Ian; Pouget-Abadie, Jean; Mirza, Mehdi; Xu, Bing; Warde-Farley, David; Ozair, Sherjil; Courville, Aaron; Bengio, Yoshua (2014). Generative Adversarial Nets (PDF). Proceedings of the International Conference on Neural Information Processing Systems (NIPS 2014). pp. 2672-2680.
  • Guo, Z., Xie, J., Wan, Y., Zhang, M., Qiao, L., Yu, J., Chen, S., Li, B., & Yao, Y. (2022). A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis. Central European Journal of Biology, 17(1), 1600–1611. https://doi.org/10.1515/biol-2022-0517
  • Huang, Z., & Chen, D. (2022). A breast cancer diagnosis method based on VIM feature selection and hierarchical clustering Random Forest algorithm. IEEE Access. https://doi.org/10.1109/access.2021.3139595
  • Jalalian, A., Mashohor, S., Mahmud, R., Saripan, M. I., Ramli, A. R., & Karasfi, B. (2013). Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clinical Imaging, 37(3), 420–426. https://doi.org/10.1016/j.clinimag.2012.09.024
  • Karita, S., Chen, N., Hayashi, T., Hori, T., Inaguma, H., Jiang, Z., Someki, M., Soplin, N. E. Y., Yamamoto, R., Wang, X., Watanabe, S., Yoshimura, T., & Zhang, W. (2019). A Comparative Study on Transformer vs RNN in Speech Applications. 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). https://doi.org/10.1109/asru46091.2019.9003750
  • Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
  • Lewis, H. G., & Brown, M. (2001). A generalized confusion matrix for assessing area estimates from remotely sensed data. International Journal of Remote Sensing, 22(16), 3223–3235. https://doi.org/10.1080/01431160152558332
  • Mohammad, W. T., Teete, R., Al-Aaraj, H., Rubbai, Y., & Arabyat, M. M. (2022b). Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques. Applied Bionics and Biomechanics, 2022, 1–9. https://doi.org/10.1155/2022/6187275
  • Nitish, S., Geoffrey ,H., Alex, K., Ilya, S., and Ruslan, S. (2014). Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1 (January 2014), 1929-1958.
  • Nurdian, S. W., Adu, N., Palupi, I. R., & Raharjo, W. (2016). Comparison tomography relocation hypocenter grid search and guided grid search method in Java island. Journal of Physics: Conference Series, 776, 012113. https://doi.org/10.1088/1742-6596/776/1/012113
  • Rasool, A., Bunterngchit, C., Luo, T., Islam, M. R., Qu, Q., & Jiang, Q. (2022). Improved Machine Learning-Based Predictive Models for breast cancer diagnosis. International Journal of Environmental Research and Public Health, 19(6), 3211. https://doi.org/10.3390/ijerph19063211
  • Roujol, S., Weingärtner, S., Foppa, M., Chow, K., Kawaji, K., Ngo, L., Kellman, P., Manning, W. J., Thompson, R. B., & Nezafat, R. (2014). Accuracy, Precision, and reproducibility of four T1 mapping sequences: A Head-to-Head comparison of MOLLI, ShMOLLI, SASHA, and SAPPHIRE. Radiology, 272(3), 683–689. https://doi.org/10.1148/radiol.14140296
  • Sahoo, S. K., Saha, A. K., Ezugwu, A. E., Agushaka, J. O., Abuhaija, B., Alsoud, A. R., & Abualigah, L. (2022). Moth Flame Optimization: Theory, modifications, hybridizations, and applications. Archives of Computational Methods in Engineering, 30(1), 391–426. https://doi.org/10.1007/s11831-022-09801-z
  • Sahu, Y., Tripathi, A., Gupta, R. K., Gautam, P., Pateriya, R. K., & Gupta, A. (2022). A CNN-SVM based computer aided diagnosis of breast Cancer using histogram K-means segmentation technique. Multimedia Tools and Applications, 82(9), 14055–14075. https://doi.org/10.1007/s11042-022-13807-x
  • Sajjad, M., Khan, Z. A., Ullah, A., Hussain, T., Ullah, W., Lee, M. Y., & Baik, S. W. (2020). A novel CNN-GRU-Based hybrid approach for Short-Term residential load Forecasting. IEEE Access, 8, 143759–143768. https://doi.org/10.1109/access.2020.3009537
  • Sendra, A. L., Carrasco, A., Martín, A. J., & De Las Heras, A. (2019). The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition, 91, 216–231. https://doi.org/10.1016/j.patcog.2019.02.023
  • Shahid, F., Zameer, A., & Muneeb, M. (2020). Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. Chaos Solitons & Fractals, 140, 110212. https://doi.org/10.1016/j.chaos.2020.110212
  • Shewalkar, A. N., Nyavanandi, D., & Ludwig, S. A. (2019). Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU. Journal of Artificial Intelligence and Soft Computing Research, 9(4), 235–245. https://doi.org/10.2478/jaiscr-2019-0006
  • Sherstinsky, A. (2020). Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306. https://doi.org/10.1016/j.physd.2019.132306
  • Suresh, T., Brijet, Z., & Subha, T. D. (2022, November 2). Imbalanced medical disease dataset classification using enhanced generative adversarial network. Computer Methods in Biomechanics and Biomedical Engineering, 1–17. https://doi.org/10.1080/10255842.2022.2134729
  • Toğaçar, M., Ergen, B., & Cömert, Z. (2020). Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Biocybernetics and Biomedical Engineering, 40(1), 23–39. https://doi.org/10.1016/j.bbe.2019.11.004
  • Wolberg,W., Mangasarian,Olvi, Street,Nick, and Street,W.. (1995). Breast Cancer Wisconsin (Diagnostic). UCI Machine Learning Repository. https://doi.org/10.24432/C5DW2B.
  • Yusoff, M., Haryanto, T., Suhartanto, H., Mustafa, W. A., Zain, J. M., & Kusmardi, K. (2023). Accuracy Analysis of deep learning methods in breast Cancer Classification: A Structured review. Diagnostics, 13(4), 683. https://doi.org/10.3390/diagnostics13040683
  • Yu, B., Xie, H., & Xu, Z. (2023b). PN-GCN: Positive-negative graph convolution neural network in information system to classification. Information Sciences, 632, 411–423. https://doi.org/10.1016/j.ins.2023.03.013
  • Breast Cancer Dataset, https://www.who.int/news-room/fact-sheets/detail/breast-cancer (12.07.2023.).

Meme Kanseri Erken Teşhisi için MAMA ve KTB Kullanarak Geliştirilen Model

Yıl 2024, Cilt: 24 Sayı: 2, 320 - 329, 29.04.2024

Samet Aymaz

https://doi.org/10.35414/akufemubid.1360156

Öz

Meme kanseri, dünya genelinde kadınlarda en sık görülen kanser türlerinden biridir ve bu hastalıkta erken teşhis hayat kurtarıcı olabilir. Bu çalışma, Wisconsin Meme Kanseri Teşhisi (WMKT) veri setine odaklanarak meme kanseri teşhisi için doğru ve güvenilir bir model geliştirme amacı gütmektedir. Çalışmada, ilk aşamada Minimum Artıklık Maksimum Alaka Düzeyi (MAMA) yöntemi kullanılarak özellik seçimi yapılmıştır. Yöntem, veri madenciliği ve özellik seçimi alanında etkili bir araç olarak kullanılmaktadır. MAMA ile özelliklerin önem sıralaması yapılarak, sadece anlamlı olanlar kullanılmıştır. Özellik seçimi, modelin karmaşıklığını azaltırken performansı artırır. Daha sonra, MAMA ile seçilen bu özellikler, meme kanseri sınıflandırması için oluşturulan Kapılı Tekrarlayan Birim (KTB) tabanlı bir sinir ağı modeli ile sınıflandırılmaktadır. KTB, tek boyutlu özellik serilerini işleme yeteneğine sahiptir ve karmaşık sınıflandırma problemlerinde etkili sonuçlar verir. Sonuçlar, bu yenilikçi yaklaşımın meme kanseri teşhisinde oldukça başarılı olduğunu göstermektedir. Yapılan değerlendirmelerde doğruluk metriği için %98.28, kesinlik metriği için %98.59, duyarlık metriği için %98.59, özgüllük metriği için %97.67 ve F-puanı metriği için %98.59 değerleri elde edilmiştir. Sonuçlar yöntemin klinik uygulamalarda uzmanlara yardımcı olabileceğini ortaya koymaktadır. Önerilen yaklaşımın toplumun her kesimi için erişilebilirlik, basit sistemlerde bile hızlı ve yüksek doğrulukla çalışabilmek gibi önemli avantajları olduğu sonuçlardan anlaşılmaktadır.

Anahtar Kelimeler

Meme kanseri Özellik seçimi MAMA yöntemi; KTB modeli

Kaynakça

  • Agarap, A. F. (2018). Deep learning using rectified linear units (relu). ArXiv Preprint ArXiv:1803.08375.
  • Ahmed, Y. A., Koçer, B., Huda, S., Al-Rimy, B. a. S., & Hassan, M. M. (2020). A system call refinement-based enhanced Minimum Redundancy Maximum Relevance method for ransomware early detection. Journal of Network and Computer Applications, 167, 102753. https://doi.org/10.1016/j.jnca.2020.102753
  • Albadr, M. A. A., Ayob, M., Tiun, S., AL-Dhief, F. T., Arram, A., & Khalaf, S. (2023, April 27). Breast cancer diagnosis using the fast learning network algorithm. Frontiers in Oncology, 13. https://doi.org/10.3389/fonc.2023.1150840
  • Alshayeji, M. H., Ellethy, H., Abed, S., & Gupta, R. (2022, January). Computer-aided detection of breast cancer on the Wisconsin dataset: An artificial neural networks approach. Biomedical Signal Processing and Control, 71, 103141. https://doi.org/10.1016/j.bspc.2021.103141
  • Aswathy, M., & Mani, M. P. (2017). Detection of breast cancer on digital histopathology images: Present status and future possibilities. Informatics in Medicine Unlocked, 8, 74–79. https://doi.org/10.1016/j.imu.2016.11.001
  • Bhardwaj, A., Bhardwaj, H., Sakalle, A., Uddin, Z., Sakalle, M., & Ibrahim, W. (2022). Tree-Based and Machine learning algorithm analysis for breast cancer classification. Computational Intelligence and Neuroscience, 2022, 1–6. https://doi.org/10.1155/2022/6715406
  • Billah, M., & Waheed, S. (2020). Minimum redundancy maximum relevance (mRMR) based feature selection from endoscopic images for automatic gastrointestinal polyp detection. Multimedia Tools and Applications, 79(33–34), 23633–23643. https://doi.org/10.1007/s11042-020-09151-7
  • Chen, J., Jing, H., Chang, Y., & Liu, Q. (2019). Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process. Reliability Engineering & System Safety, 185, 372–382. https://doi.org/10.1016/j.ress.2019.01.006
  • Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1406.1078
  • Cho, K., Van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259.
  • Ding, C., and H. Peng. "Minimum redundancy feature selection from microarray gene expression data." Journal of Bioinformatics and Computational Biology. Vol. 3, Number 2, 2005, pp. 185-205.
  • Freund, Y., Schapire, R.E. (1995). A desicion-theoretic generalization of on-line learning and an application to boosting. In: Vitânyi, P. (eds) Computational Learning Theory. EuroCOLT 1995. Lecture Notes in Computer Science, vol 904. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59119-2_166
  • Goutte, C., & Gaussier, E. (2005). A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation. In Springer eBooks (pp. 345–359). https://doi.org/10.1007/978-3-540-31865-1_25
  • Goodfellow, Ian; Pouget-Abadie, Jean; Mirza, Mehdi; Xu, Bing; Warde-Farley, David; Ozair, Sherjil; Courville, Aaron; Bengio, Yoshua (2014). Generative Adversarial Nets (PDF). Proceedings of the International Conference on Neural Information Processing Systems (NIPS 2014). pp. 2672-2680.
  • Guo, Z., Xie, J., Wan, Y., Zhang, M., Qiao, L., Yu, J., Chen, S., Li, B., & Yao, Y. (2022). A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis. Central European Journal of Biology, 17(1), 1600–1611. https://doi.org/10.1515/biol-2022-0517
  • Huang, Z., & Chen, D. (2022). A breast cancer diagnosis method based on VIM feature selection and hierarchical clustering Random Forest algorithm. IEEE Access. https://doi.org/10.1109/access.2021.3139595
  • Jalalian, A., Mashohor, S., Mahmud, R., Saripan, M. I., Ramli, A. R., & Karasfi, B. (2013). Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clinical Imaging, 37(3), 420–426. https://doi.org/10.1016/j.clinimag.2012.09.024
  • Karita, S., Chen, N., Hayashi, T., Hori, T., Inaguma, H., Jiang, Z., Someki, M., Soplin, N. E. Y., Yamamoto, R., Wang, X., Watanabe, S., Yoshimura, T., & Zhang, W. (2019). A Comparative Study on Transformer vs RNN in Speech Applications. 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). https://doi.org/10.1109/asru46091.2019.9003750
  • Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
  • Lewis, H. G., & Brown, M. (2001). A generalized confusion matrix for assessing area estimates from remotely sensed data. International Journal of Remote Sensing, 22(16), 3223–3235. https://doi.org/10.1080/01431160152558332
  • Mohammad, W. T., Teete, R., Al-Aaraj, H., Rubbai, Y., & Arabyat, M. M. (2022b). Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques. Applied Bionics and Biomechanics, 2022, 1–9. https://doi.org/10.1155/2022/6187275
  • Nitish, S., Geoffrey ,H., Alex, K., Ilya, S., and Ruslan, S. (2014). Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1 (January 2014), 1929-1958.
  • Nurdian, S. W., Adu, N., Palupi, I. R., & Raharjo, W. (2016). Comparison tomography relocation hypocenter grid search and guided grid search method in Java island. Journal of Physics: Conference Series, 776, 012113. https://doi.org/10.1088/1742-6596/776/1/012113
  • Rasool, A., Bunterngchit, C., Luo, T., Islam, M. R., Qu, Q., & Jiang, Q. (2022). Improved Machine Learning-Based Predictive Models for breast cancer diagnosis. International Journal of Environmental Research and Public Health, 19(6), 3211. https://doi.org/10.3390/ijerph19063211
  • Roujol, S., Weingärtner, S., Foppa, M., Chow, K., Kawaji, K., Ngo, L., Kellman, P., Manning, W. J., Thompson, R. B., & Nezafat, R. (2014). Accuracy, Precision, and reproducibility of four T1 mapping sequences: A Head-to-Head comparison of MOLLI, ShMOLLI, SASHA, and SAPPHIRE. Radiology, 272(3), 683–689. https://doi.org/10.1148/radiol.14140296
  • Sahoo, S. K., Saha, A. K., Ezugwu, A. E., Agushaka, J. O., Abuhaija, B., Alsoud, A. R., & Abualigah, L. (2022). Moth Flame Optimization: Theory, modifications, hybridizations, and applications. Archives of Computational Methods in Engineering, 30(1), 391–426. https://doi.org/10.1007/s11831-022-09801-z
  • Sahu, Y., Tripathi, A., Gupta, R. K., Gautam, P., Pateriya, R. K., & Gupta, A. (2022). A CNN-SVM based computer aided diagnosis of breast Cancer using histogram K-means segmentation technique. Multimedia Tools and Applications, 82(9), 14055–14075. https://doi.org/10.1007/s11042-022-13807-x
  • Sajjad, M., Khan, Z. A., Ullah, A., Hussain, T., Ullah, W., Lee, M. Y., & Baik, S. W. (2020). A novel CNN-GRU-Based hybrid approach for Short-Term residential load Forecasting. IEEE Access, 8, 143759–143768. https://doi.org/10.1109/access.2020.3009537
  • Sendra, A. L., Carrasco, A., Martín, A. J., & De Las Heras, A. (2019). The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition, 91, 216–231. https://doi.org/10.1016/j.patcog.2019.02.023
  • Shahid, F., Zameer, A., & Muneeb, M. (2020). Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. Chaos Solitons & Fractals, 140, 110212. https://doi.org/10.1016/j.chaos.2020.110212
  • Shewalkar, A. N., Nyavanandi, D., & Ludwig, S. A. (2019). Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU. Journal of Artificial Intelligence and Soft Computing Research, 9(4), 235–245. https://doi.org/10.2478/jaiscr-2019-0006
  • Sherstinsky, A. (2020). Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306. https://doi.org/10.1016/j.physd.2019.132306
  • Suresh, T., Brijet, Z., & Subha, T. D. (2022, November 2). Imbalanced medical disease dataset classification using enhanced generative adversarial network. Computer Methods in Biomechanics and Biomedical Engineering, 1–17. https://doi.org/10.1080/10255842.2022.2134729
  • Toğaçar, M., Ergen, B., & Cömert, Z. (2020). Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Biocybernetics and Biomedical Engineering, 40(1), 23–39. https://doi.org/10.1016/j.bbe.2019.11.004
  • Wolberg,W., Mangasarian,Olvi, Street,Nick, and Street,W.. (1995). Breast Cancer Wisconsin (Diagnostic). UCI Machine Learning Repository. https://doi.org/10.24432/C5DW2B.
  • Yusoff, M., Haryanto, T., Suhartanto, H., Mustafa, W. A., Zain, J. M., & Kusmardi, K. (2023). Accuracy Analysis of deep learning methods in breast Cancer Classification: A Structured review. Diagnostics, 13(4), 683. https://doi.org/10.3390/diagnostics13040683
  • Yu, B., Xie, H., & Xu, Z. (2023b). PN-GCN: Positive-negative graph convolution neural network in information system to classification. Information Sciences, 632, 411–423. https://doi.org/10.1016/j.ins.2023.03.013
  • Breast Cancer Dataset, https://www.who.int/news-room/fact-sheets/detail/breast-cancer (12.07.2023.).
Toplam 38 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Görüşü ve Çoklu Ortam Hesaplama (Diğer)
Bölüm Makaleler
Yazarlar

Samet Aymaz 0000-0003-0735-0487

Erken Görünüm Tarihi 14 Nisan 2024
Yayımlanma Tarihi 29 Nisan 2024
Gönderilme Tarihi 14 Eylül 2023
Yayımlandığı Sayı Yıl 2024 Cilt: 24 Sayı: 2

Kaynak Göster

APA Aymaz, S. (2024). Meme Kanseri Erken Teşhisi için MAMA ve KTB Kullanarak Geliştirilen Model. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 24(2), 320-329. https://doi.org/10.35414/akufemubid.1360156
AMA Aymaz S. Meme Kanseri Erken Teşhisi için MAMA ve KTB Kullanarak Geliştirilen Model. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. Nisan 2024;24(2):320-329. doi:10.35414/akufemubid.1360156
Chicago Aymaz, Samet. “Meme Kanseri Erken Teşhisi için MAMA Ve KTB Kullanarak Geliştirilen Model”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 24, sy. 2 (Nisan 2024): 320-29. https://doi.org/10.35414/akufemubid.1360156.
EndNote Aymaz S (01 Nisan 2024) Meme Kanseri Erken Teşhisi için MAMA ve KTB Kullanarak Geliştirilen Model. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 24 2 320–329.
IEEE S. Aymaz, “Meme Kanseri Erken Teşhisi için MAMA ve KTB Kullanarak Geliştirilen Model”, Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, c. 24, sy. 2, ss. 320–329, 2024, doi: 10.35414/akufemubid.1360156.
ISNAD Aymaz, Samet. “Meme Kanseri Erken Teşhisi için MAMA Ve KTB Kullanarak Geliştirilen Model”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 24/2 (Nisan 2024), 320-329. https://doi.org/10.35414/akufemubid.1360156.
JAMA Aymaz S. Meme Kanseri Erken Teşhisi için MAMA ve KTB Kullanarak Geliştirilen Model. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2024;24:320–329.
MLA Aymaz, Samet. “Meme Kanseri Erken Teşhisi için MAMA Ve KTB Kullanarak Geliştirilen Model”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, c. 24, sy. 2, 2024, ss. 320-9, doi:10.35414/akufemubid.1360156.
Vancouver Aymaz S. Meme Kanseri Erken Teşhisi için MAMA ve KTB Kullanarak Geliştirilen Model. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2024;24(2):320-9.
 Kapak Resmi İndir