Research Article
Year 2022,
Issue: 006, 10 - 23, 31.12.2022
Abstract
Günümüzde böbrek tümörü vakalarına oldukça sık rastlanmaktadır. Hastanın böbreğindeki tümör; büyüklük ve bölgesel olarak farklılık gösterebilir. Tümörün bir uzman tarafından doğru tespit edilememesi, geç teşhis ve erken tedavi uygulanmaması halinde hastalar çok ciddi riskler ile karşı karşıyadır. Doğru ve hızlı teşhis için literatürde yapay zeka teknolojilerine dayalı karar destek sistemlerinin önemli çözüm önerileri sunduğu görülmektedir. Tıp alanı yapay zekanın en çok kullanıldığı alanların başını çekmektedir. Böbrek tümörlerin tespitinde bilgisayarlı tomografi tıbbı görüntüleme cihazları yaygın olarak kullanılmaktadır. Bu çalışma kapsamında, bilgisayarlı tomografi cihazı tarafından üretilen görüntüler kullanılmıştır. Böbrek tümörü tespit analizi için sınıflandırıcı model olarak Convolutional Neural Network (CNN) kullanılmıştır. VGG16, VGG19 ve ResNET50 modelleri kullanarak aralarındaki performans karşılaştırma analizi yapılmıştır. Uygulamanın yazılım dili olarak Python ile Tensorflow, Keras ve OpenCV kütüphaneleri kullanılmıştır. Yapılan analiz tespiti sonucunda böbrek tümörlerinin saptanmasında %85’lere varan oranda tespitin başarılı şekilde gerçekleştirildiği görülmüştür.
Thanks
Bu çalışma, Kütahya Lisansüstü Eğitim Enstitüsü Bilgisayar Mühendisliği Anabilim Dalında Dr. Öğr. Üyesi Durmuş ÖZDEMİR Danışmanlığında Yüksek Lisans yapan Caner KARADAĞ’ın yüksek lisans tez çalışmalarından üretilmiştir
References
- [1] Hinton, G. Deng, L. Yu, D. Dahl, G.E. Mohamed, A.-r. Jaitly, N. Senior, A. Vanhoucke, V. Nguyen, P. ve Sainath, T.N., (2012). "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups", IEEE Signal Processing Magazine, 29: 82-97.
- [2] Özdemir, D., Arslan, N. N., (2021), Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images, 630p, 2021.
- [3] Canbay, P., (2020), Sağlıkta yapay zeka: derin öğrenme teknikleri ve uygulamaları, pp. 25-26
- [4] Dandil, E., (2015), Mr görüntüleri ve mr spektroskopi verileri ile yapay öğrenme tabanlı beyin tümörü tespit yöntemi ve uygulaması, pp. 4-7
- [5] Böbrek Kanseri, Erişim Adresi: https://www.medicalpark.com.tr/bobrek-kanseri-nedir/hg-1812, Erişim: 24.03.2022.
- [6] Eker, A. G., Duru, N., (2021), Medikal görüntü işlemede Derin öğrenme uygulamaları, pp. 460-466, 2021.
- [7] Yıldırım, (2008), Medikal Görüntüleme Sistemleri İçin Yarıiletken Matris Dedektör Tasarımı ve Geliştirilmesi, 1p,7p.
- [8] Shi Yin, vd., (2020), Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks, Med. Img. Anal. 60, 101602.
- [9] Njoud Abdullah Almansour, vd., (2019), Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study, Comput. Biol. Med. 109 101–111.
- [10] Thong, W., Kadoury, S., Piché, N., ve Pal, C. J. (2016). Convolutional networks for kidney segmentation in contrast-enhanced ct scans. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6. doi:10.1080/21681163.2016.1148636.
- [11] Zhou, X., Takayama, R., Wang, S., Zhou, X., Hara, T., ve Fujita, H. (2017). Automated segmentation of 3d anatomical structures on ct images by using a deep convolutional network based on end-to-end learning approach. In Medical imaging 2017: Image processing: 10133 (p. 1013324). International Society for Optics and Photonics. doi:10.1117/12.2254201.
- [12] Kidney Tumor and Stone MRI Images, Erişim Adresi: https://www.kaggle.com/code/ahmedbadr22/kidney-disease-classifier-with-99-cnn/data, Erişim: 26.04.2022
- [13] He, X. Zhang, S. Ren, and J. Sun, (2016), “Deep residual learning for image recognition”. In CVPR,
- [14] Simonyan, K., Zisserman, A.,( 2014), Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556
- [15] Bu X, Peng J, Yan J, Tan T, Zhang Z. GAIA: A transfer learning system of object detection that fits your needs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2021. p. 274–83.
- [16] Azeez RA, Abdul-Hussein MK, Mahdi MS, ALRikabi HTHS, (2021), Design a system for an approved video copyright over cloud based on biometric iris and random walk generator using watermark technique. Period Eng Nat Sci 10(1):178–87.
- [17] Pan, T., Yang, G., Wang, C., et al., (2019), “A multi-task convolutional neural network for renal tumor segmentation and classification using multi-phasic ct images,” in 2019 IEEE International Conference on Image Processing (ICIP), pp. 809–813. doi: 10.1109/ICIP.2019.8802924.
- [18] Khan, M.A., Ashraf, I., Alhaisoni, M., Damaševičius, R., Scherer, R., Rehman, A., S.Bukhari, S. A. C., (2020), Multimodal brain tumor classification using deep learning and robust feature selection: a machine learning application for radiologists Diagnostics, 10 (8) p. 565, 10.3390/diagnostics10080565
- [19] Sahu, B. P., Mohanty, S. N., Rout, S. K., (2019), A hybrid approach for breast cancer classification and diagnosis EAI Endorsed Trans. Scalable Inf. Syst., 6(20) pp. 1-8
- [20] Sahu, B. P., Das, S., Mohanty, S. N., Rout, S. K., (2018), Ensemble comparative study for diagnosis of breast cancer data set Int. J. Eng. Technol., 7 pp. 281-288
- [21] CNN mimarisi, Erişim Adresi: https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53, Erişim: 19.10.2022.
- [22] VGG16 mimarisi, Erişim Adresi: http://www.cs.toronto.edu/~frossard/post/vgg16/, Erişim: 19.10.2022.
- [23] VGG19 mimarisi, Erişim Adresi: https://www.researchgate.net/figure/llustration-of-the-network-architecture-of-VGG-19-model-conv-means-convolution-FC-means_fig2_325137356, Erişim: 19.10.2022.
- [24] ResNET50 mimarisi, Erişim Adresi: https://www.researchgate.net/figure/ResNet-50-architecture-26-shown-with-the-residual-units-the-size-of-the-filters-and_fig1_338603223, Erişim: 19.10.2022.
Year 2022,
Issue: 006, 10 - 23, 31.12.2022
Abstract
Kidney tumors are very common nowadays. Tumor in the patient's kidney; may vary in size and region. If the tumor cannot be detected correctly by a specialist, late diagnosis and early treatment are not applied, patients are at risk of death. In such cases, the importance of technology becomes clear. With the rapid development of technology, the concept of artificial intelligence has become the center of our lives in recent years. Medicine is also one of the areas where this concept is used the most. Computed tomography medical imaging devices are widely used in the detection of kidney tumors. Within the scope of this study, images obtained by computerized tomography device were used. Convolutional Neural Network (CNN) was used as the classifier model for kidney tumor detection analysis. Performance comparison analysis between them was made using VGG16, VGG19 and ResNET50 models. Python and Tensorflow, Keras and OpenCV libraries were used as the software language of the application. As a result of the analysis, it was seen that it was successfully performed in the detection of kidney tumors
References
- [1] Hinton, G. Deng, L. Yu, D. Dahl, G.E. Mohamed, A.-r. Jaitly, N. Senior, A. Vanhoucke, V. Nguyen, P. ve Sainath, T.N., (2012). "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups", IEEE Signal Processing Magazine, 29: 82-97.
- [2] Özdemir, D., Arslan, N. N., (2021), Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images, 630p, 2021.
- [3] Canbay, P., (2020), Sağlıkta yapay zeka: derin öğrenme teknikleri ve uygulamaları, pp. 25-26
- [4] Dandil, E., (2015), Mr görüntüleri ve mr spektroskopi verileri ile yapay öğrenme tabanlı beyin tümörü tespit yöntemi ve uygulaması, pp. 4-7
- [5] Böbrek Kanseri, Erişim Adresi: https://www.medicalpark.com.tr/bobrek-kanseri-nedir/hg-1812, Erişim: 24.03.2022.
- [6] Eker, A. G., Duru, N., (2021), Medikal görüntü işlemede Derin öğrenme uygulamaları, pp. 460-466, 2021.
- [7] Yıldırım, (2008), Medikal Görüntüleme Sistemleri İçin Yarıiletken Matris Dedektör Tasarımı ve Geliştirilmesi, 1p,7p.
- [8] Shi Yin, vd., (2020), Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks, Med. Img. Anal. 60, 101602.
- [9] Njoud Abdullah Almansour, vd., (2019), Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study, Comput. Biol. Med. 109 101–111.
- [10] Thong, W., Kadoury, S., Piché, N., ve Pal, C. J. (2016). Convolutional networks for kidney segmentation in contrast-enhanced ct scans. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6. doi:10.1080/21681163.2016.1148636.
- [11] Zhou, X., Takayama, R., Wang, S., Zhou, X., Hara, T., ve Fujita, H. (2017). Automated segmentation of 3d anatomical structures on ct images by using a deep convolutional network based on end-to-end learning approach. In Medical imaging 2017: Image processing: 10133 (p. 1013324). International Society for Optics and Photonics. doi:10.1117/12.2254201.
- [12] Kidney Tumor and Stone MRI Images, Erişim Adresi: https://www.kaggle.com/code/ahmedbadr22/kidney-disease-classifier-with-99-cnn/data, Erişim: 26.04.2022
- [13] He, X. Zhang, S. Ren, and J. Sun, (2016), “Deep residual learning for image recognition”. In CVPR,
- [14] Simonyan, K., Zisserman, A.,( 2014), Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556
- [15] Bu X, Peng J, Yan J, Tan T, Zhang Z. GAIA: A transfer learning system of object detection that fits your needs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2021. p. 274–83.
- [16] Azeez RA, Abdul-Hussein MK, Mahdi MS, ALRikabi HTHS, (2021), Design a system for an approved video copyright over cloud based on biometric iris and random walk generator using watermark technique. Period Eng Nat Sci 10(1):178–87.
- [17] Pan, T., Yang, G., Wang, C., et al., (2019), “A multi-task convolutional neural network for renal tumor segmentation and classification using multi-phasic ct images,” in 2019 IEEE International Conference on Image Processing (ICIP), pp. 809–813. doi: 10.1109/ICIP.2019.8802924.
- [18] Khan, M.A., Ashraf, I., Alhaisoni, M., Damaševičius, R., Scherer, R., Rehman, A., S.Bukhari, S. A. C., (2020), Multimodal brain tumor classification using deep learning and robust feature selection: a machine learning application for radiologists Diagnostics, 10 (8) p. 565, 10.3390/diagnostics10080565
- [19] Sahu, B. P., Mohanty, S. N., Rout, S. K., (2019), A hybrid approach for breast cancer classification and diagnosis EAI Endorsed Trans. Scalable Inf. Syst., 6(20) pp. 1-8
- [20] Sahu, B. P., Das, S., Mohanty, S. N., Rout, S. K., (2018), Ensemble comparative study for diagnosis of breast cancer data set Int. J. Eng. Technol., 7 pp. 281-288
- [21] CNN mimarisi, Erişim Adresi: https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53, Erişim: 19.10.2022.
- [22] VGG16 mimarisi, Erişim Adresi: http://www.cs.toronto.edu/~frossard/post/vgg16/, Erişim: 19.10.2022.
- [23] VGG19 mimarisi, Erişim Adresi: https://www.researchgate.net/figure/llustration-of-the-network-architecture-of-VGG-19-model-conv-means-convolution-FC-means_fig2_325137356, Erişim: 19.10.2022.
- [24] ResNET50 mimarisi, Erişim Adresi: https://www.researchgate.net/figure/ResNet-50-architecture-26-shown-with-the-residual-units-the-size-of-the-filters-and_fig1_338603223, Erişim: 19.10.2022.
There are 24 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | December 31, 2022 |
| Submission Date | September 24, 2022 |
| Published in Issue | Year 2022 Issue: 006 |
