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Ultrason RF Sinyallerinden Göğüs Kanserinin Derin Öğrenme Tabanlı Yaklaşımlarla Tespit Edilmesi

Year 2022, Volume: 34 Issue: 2, 761 - 768, 30.09.2022

Fatih Demir

https://doi.org/10.35234/fumbd.1142207
Cited By: 1

Abstract

Göğüs kanseri kadınların en çok yakalandığı kanser türüdür. Bu hastalıkta erken teşhis çok önemlidir. Erken teşhis için kullanılan en önemli tıbbi teknolojiler arasında Manyetik Rezonans (MR) ve Ultrason (US) yer almaktadır. US ile teşhis MR ile teşhise göre daha az maliyetlidir fakat daha fazla deneyim gerektirir. Gelişen teknoloji ile yapay zekâyı kullanan otomatik karar destek sistemleri son derece popüler hale gelmiştir. Bu noktada bu çalışmada US RF sinyallerini kullanarak derin öğrenme tabanlı bir yaklaşımla göğüs kanseri otomatik teşhis edilmeye çalışılmıştır. Çalışmada kullanılan örnek sayısı fazla olmadığı için önceden eğitilmiş bir ESA modeli olan MobileNetV2 öznitelik çıkarmak için kullanılmıştır. Sınıflandırma aşamasında ise bir topluluk sınıflandırıcısı olan ensemble RUSBoosted Tree (ERBT) algoritması tercih edilmiştir.

Keywords

US RF sinyaller Göğüs Kanseri Derin Öğrenme Sınıflandırma

References

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  • [12] N. Uniyal, H. Eskandari, P. Abolmaesumi, S. Sojoudi, P. Gordon, L. Warren, R.N. Rohling, S.E. Salcudean, M. Moradi, Ultrasound RF time series for classification of breast lesions, IEEE Trans. Med. Imaging. 34 (2014) 652–661.
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  • [19] F. Demir, B. Tașcı, An Effective and Robust Approach Based on R-CNN+LSTM Model and NCAR Feature Selection for Ophthalmological Disease Detection from Fundus Images, J. Pers. Med. 11 (2021) 1276. https://doi.org/10.3390/jpm11121276.
  • [20] F. Demir, K. Demir, A. Şengür, DeepCov19Net: Automated COVID-19 Disease Detection with a Robust and Effective Technique Deep Learning Approach, New Gener. Comput. (2022) 1–23. https://doi.org/10.1007/s00354-021-00152-0.
  • [21] F. Demir, K. Siddique, M. Alswaitti, K. Demir, A. Sengur, A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection, J. Pers. Med. 12 (2022) 55. https://doi.org/10.3390/jpm12010055.
  • [22] F. Demir, Deep autoencoder-based automated brain tumor detection from MRI data, in: Artif. Intell. Brain-Computer Interface, Elsevier, 2022: pp. 317–351. https://doi.org/10.1016/b978-0-323-91197-9.00013-8.
  • [23] F. Demir, DeepBreastNet: A novel and robust approach for automated breast cancer detection from histopathological images, Biocybern. Biomed. Eng. 41 (2021) 1123–1139. https://doi.org/10.1016/j.bbe.2021.07.004.
  • [24] N. Antropova, B.Q. Huynh, M.L. Giger, A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets, Med. Phys. 44 (2017) 5162–5171.
  • [25] S. Han, H.-K. Kang, J.-Y. Jeong, M.-H. Park, W. Kim, W.-C. Bang, Y.-K. Seong, A deep learning framework for supporting the classification of breast lesions in ultrasound images, Phys. Med. Biol. 62 (2017) 7714.
  • [26] M. Byra, Discriminant analysis of neural style representations for breast lesion classification in ultrasound, Biocybern. Biomed. Eng. 38 (2018) 684–690.
  • [27] M.H. Yap, G. Pons, J. Marti, S. Ganau, M. Sentis, R. Zwiggelaar, A.K. Davison, R. Marti, Automated breast ultrasound lesions detection using convolutional neural networks, IEEE J. Biomed. Heal. Informatics. 22 (2017) 1218–1226.
  • [28] M.H. Yap, M. Goyal, F.M. Osman, R. Martí, E. Denton, A. Juette, R. Zwiggelaar, Breast ultrasound lesions recognition: end-to-end deep learning approaches, J. Med. Imaging. 6 (2018) 11007.
  • [29] M. Byra, M. Galperin, H. Ojeda‐Fournier, L. Olson, M. O’Boyle, C. Comstock, M. Andre, Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion, Med. Phys. 46 (2019) 746–755.
  • [30] X. Qi, L. Zhang, Y. Chen, Y. Pi, Y. Chen, Q. Lv, Z. Yi, Automated diagnosis of breast ultrasonography images using deep neural networks, Med. Image Anal. 52 (2019) 185–198. [31] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, others, Imagenet large scale visual recognition challenge, Int. J. Comput. Vis. 115 (2015) 211–252.
  • [32] H. Piotrzkowska-Wróblewska, K. Dobruch-Sobczak, M. Byra, A. Nowicki, Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions, Med. Phys. 44 (2017) 6105–6109. https://doi.org/10.1002/mp.12538.
  • [33] A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, Mobilenets: Efficient convolutional neural networks for mobile vision applications, ArXiv Prepr. ArXiv1704.04861. (2017).
  • [34] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.-C. Chen, Mobilenetv2: Inverted residuals and linear bottlenecks, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2018: pp. 4510–4520.
  • [35] C. Seiffert, T.M. Khoshgoftaar, J. Van Hulse, A. Napolitano, RUSBoost: Improving classification performance when training data is skewed, in: 2008 19th Int. Conf. Pattern Recognit., IEEE, 2008: pp. 1–4.

Year 2022, Volume: 34 Issue: 2, 761 - 768, 30.09.2022

Fatih Demir

https://doi.org/10.35234/fumbd.1142207
Cited By: 1

Abstract

References

  • [1] H. Sung, J. Ferlay, R.L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, F. Bray, Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries, CA. Cancer J. Clin. 71 (2021) 209–249. https://doi.org/10.3322/caac.21660.
  • [2] H.-D. Cheng, J. Shan, W. Ju, Y. Guo, L. Zhang, Automated breast cancer detection and classification using ultrasound images: A survey, Pattern Recognit. 43 (2010) 299–317.
  • [3] J. Virmani, R. Agarwal, Assessment of despeckle filtering algorithms for segmentation of breast tumours from ultrasound images, Biocybern. Biomed. Eng. 39 (2019) 100–121.
  • [4] G.-G. Wu, L.-Q. Zhou, J.-W. Xu, J.-Y. Wang, Q. Wei, Y.-B. Deng, X.-W. Cui, C.F. Dietrich, Artificial intelligence in breast ultrasound, World J. Radiol. 11 (2019) 19.
  • [5] W.G. Flores, W.C. de Albuquerque Pereira, A.F.C. Infantosi, Improving classification performance of breast lesions on ultrasonography, Pattern Recognit. 48 (2015) 1125–1136.
  • [6] M.L. Oelze, J. Mamou, Review of quantitative ultrasound: Envelope statistics and backscatter coefficient imaging and contributions to diagnostic ultrasound, IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 63 (2016) 336–351.
  • [7] P.-H. Tsui, C.-C. Chang, Imaging local scatterer concentrations by the Nakagami statistical model, Ultrasound Med. Biol. 33 (2007) 608–619.
  • [8] X. Yu, Y. Guo, S.-M. Huang, M.-L. Li, W.-N. Lee, Beamforming effects on generalized Nakagami imaging, Phys. Med. Biol. 60 (2015) 7513.
  • [9] J. Virmani, R. Agarwal, Effect of despeckle filtering on classification of breast tumors using ultrasound images, Biocybern. Biomed. Eng. 39 (2019) 536–560.
  • [10] A. Larrue, J.A. Noble, Modeling of errors in nakagami imaging: Illustration on breast mass characterization, Ultrasound Med. Biol. 40 (2014) 917–930. https://doi.org/10.1016/j.ultrasmedbio.2013.11.018.
  • [11] M. Byra, A. Nowicki, H. Wróblewska‐Piotrzkowska, K. Dobruch‐Sobczak, Classification of breast lesions using segmented quantitative ultrasound maps of homodyned K distribution parameters, Med. Phys. 43 (2016) 5561–5569.
  • [12] N. Uniyal, H. Eskandari, P. Abolmaesumi, S. Sojoudi, P. Gordon, L. Warren, R.N. Rohling, S.E. Salcudean, M. Moradi, Ultrasound RF time series for classification of breast lesions, IEEE Trans. Med. Imaging. 34 (2014) 652–661.
  • [13] Y. Ouyang, P.-H. Tsui, S. Wu, W. Wu, Z. Zhou, Classification of benign and malignant breast tumors using h-scan ultrasound imaging, Diagnostics. 9 (2019) 182.
  • [14] Y. Liao, P. Tsui, C. Li, K. Chang, W. Kuo, C. Chang, C. Yeh, Classification of scattering media within benign and malignant breast tumors based on ultrasound texture‐feature‐based and Nakagami‐parameter images, Med. Phys. 38 (2011) 2198–2207.
  • [15] I. Trop, F. Destrempes, M. El Khoury, A. Robidoux, L. Gaboury, L. Allard, B. Chayer, G. Cloutier, The added value of statistical modeling of backscatter properties in the management of breast lesions at US, Radiology. 275 (2015) 666–674.
  • [16] I. Goodfellow, Y. Bengio, A. Courville, Deep learning, MIT press, 2016.
  • [17] K.J. Lang, A.H. Waibel, G.E. Hinton, A time-delay neural network architecture for isolated word recognition, Neural Networks. 3 (1990) 23–43.
  • [18] Y. LeCun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, Backpropagation applied to handwritten zip code recognition, Neural Comput. 1 (1989) 541–551.
  • [19] F. Demir, B. Tașcı, An Effective and Robust Approach Based on R-CNN+LSTM Model and NCAR Feature Selection for Ophthalmological Disease Detection from Fundus Images, J. Pers. Med. 11 (2021) 1276. https://doi.org/10.3390/jpm11121276.
  • [20] F. Demir, K. Demir, A. Şengür, DeepCov19Net: Automated COVID-19 Disease Detection with a Robust and Effective Technique Deep Learning Approach, New Gener. Comput. (2022) 1–23. https://doi.org/10.1007/s00354-021-00152-0.
  • [21] F. Demir, K. Siddique, M. Alswaitti, K. Demir, A. Sengur, A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection, J. Pers. Med. 12 (2022) 55. https://doi.org/10.3390/jpm12010055.
  • [22] F. Demir, Deep autoencoder-based automated brain tumor detection from MRI data, in: Artif. Intell. Brain-Computer Interface, Elsevier, 2022: pp. 317–351. https://doi.org/10.1016/b978-0-323-91197-9.00013-8.
  • [23] F. Demir, DeepBreastNet: A novel and robust approach for automated breast cancer detection from histopathological images, Biocybern. Biomed. Eng. 41 (2021) 1123–1139. https://doi.org/10.1016/j.bbe.2021.07.004.
  • [24] N. Antropova, B.Q. Huynh, M.L. Giger, A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets, Med. Phys. 44 (2017) 5162–5171.
  • [25] S. Han, H.-K. Kang, J.-Y. Jeong, M.-H. Park, W. Kim, W.-C. Bang, Y.-K. Seong, A deep learning framework for supporting the classification of breast lesions in ultrasound images, Phys. Med. Biol. 62 (2017) 7714.
  • [26] M. Byra, Discriminant analysis of neural style representations for breast lesion classification in ultrasound, Biocybern. Biomed. Eng. 38 (2018) 684–690.
  • [27] M.H. Yap, G. Pons, J. Marti, S. Ganau, M. Sentis, R. Zwiggelaar, A.K. Davison, R. Marti, Automated breast ultrasound lesions detection using convolutional neural networks, IEEE J. Biomed. Heal. Informatics. 22 (2017) 1218–1226.
  • [28] M.H. Yap, M. Goyal, F.M. Osman, R. Martí, E. Denton, A. Juette, R. Zwiggelaar, Breast ultrasound lesions recognition: end-to-end deep learning approaches, J. Med. Imaging. 6 (2018) 11007.
  • [29] M. Byra, M. Galperin, H. Ojeda‐Fournier, L. Olson, M. O’Boyle, C. Comstock, M. Andre, Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion, Med. Phys. 46 (2019) 746–755.
  • [30] X. Qi, L. Zhang, Y. Chen, Y. Pi, Y. Chen, Q. Lv, Z. Yi, Automated diagnosis of breast ultrasonography images using deep neural networks, Med. Image Anal. 52 (2019) 185–198. [31] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, others, Imagenet large scale visual recognition challenge, Int. J. Comput. Vis. 115 (2015) 211–252.
  • [32] H. Piotrzkowska-Wróblewska, K. Dobruch-Sobczak, M. Byra, A. Nowicki, Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions, Med. Phys. 44 (2017) 6105–6109. https://doi.org/10.1002/mp.12538.
  • [33] A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, Mobilenets: Efficient convolutional neural networks for mobile vision applications, ArXiv Prepr. ArXiv1704.04861. (2017).
  • [34] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.-C. Chen, Mobilenetv2: Inverted residuals and linear bottlenecks, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2018: pp. 4510–4520.
  • [35] C. Seiffert, T.M. Khoshgoftaar, J. Van Hulse, A. Napolitano, RUSBoost: Improving classification performance when training data is skewed, in: 2008 19th Int. Conf. Pattern Recognit., IEEE, 2008: pp. 1–4.
There are 34 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section MBD
Authors

Fatih Demir 0000-0003-3210-3664

Publication Date September 30, 2022
Submission Date July 7, 2022
Published in Issue Year 2022 Volume: 34 Issue: 2

Cite

APA Demir, F. (2022). Ultrason RF Sinyallerinden Göğüs Kanserinin Derin Öğrenme Tabanlı Yaklaşımlarla Tespit Edilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34(2), 761-768. https://doi.org/10.35234/fumbd.1142207
AMA Demir F. Ultrason RF Sinyallerinden Göğüs Kanserinin Derin Öğrenme Tabanlı Yaklaşımlarla Tespit Edilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. September 2022;34(2):761-768. doi:10.35234/fumbd.1142207
Chicago Demir, Fatih. “Ultrason RF Sinyallerinden Göğüs Kanserinin Derin Öğrenme Tabanlı Yaklaşımlarla Tespit Edilmesi”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 34, no. 2 (September 2022): 761-68. https://doi.org/10.35234/fumbd.1142207.
EndNote Demir F (September 1, 2022) Ultrason RF Sinyallerinden Göğüs Kanserinin Derin Öğrenme Tabanlı Yaklaşımlarla Tespit Edilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 34 2 761–768.
IEEE F. Demir, “Ultrason RF Sinyallerinden Göğüs Kanserinin Derin Öğrenme Tabanlı Yaklaşımlarla Tespit Edilmesi”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, vol. 34, no. 2, pp. 761–768, 2022, doi: 10.35234/fumbd.1142207.
ISNAD Demir, Fatih. “Ultrason RF Sinyallerinden Göğüs Kanserinin Derin Öğrenme Tabanlı Yaklaşımlarla Tespit Edilmesi”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 34/2 (September 2022), 761-768. https://doi.org/10.35234/fumbd.1142207.
JAMA Demir F. Ultrason RF Sinyallerinden Göğüs Kanserinin Derin Öğrenme Tabanlı Yaklaşımlarla Tespit Edilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2022;34:761–768.
MLA Demir, Fatih. “Ultrason RF Sinyallerinden Göğüs Kanserinin Derin Öğrenme Tabanlı Yaklaşımlarla Tespit Edilmesi”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, vol. 34, no. 2, 2022, pp. 761-8, doi:10.35234/fumbd.1142207.
Vancouver Demir F. Ultrason RF Sinyallerinden Göğüs Kanserinin Derin Öğrenme Tabanlı Yaklaşımlarla Tespit Edilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2022;34(2):761-8.

Cited By

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Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji
https://doi.org/10.29109/gujsc.1400991
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