Research Article
BibTex RIS Cite

ROI görüntülerinde derin inanç ağları kullanarak göğüs kanseri teşhisi

Year 2022, Volume: 28 Issue: 2, 286 - 291, 30.04.2022

Abstract

Elle çıkarılan öznitelikler, görüntü işleme, tanıma ve bilgisayarlı görü için etkili yöntemlerdir. Ancak, veri boyutu ve görüntü çözünürlüklerindeki artış, özniteliklerin elde edilmesinde zorluklara sebep olmuştur. Kararsız, yönteme bağımlı ve hesaplama açısından yoğundurlar. Özellikle, görüntü veri kümelerindeki büyük veriler, öngörülemeyen uzun süreçler doğurur. Görüntü işleme için öznitelik çıkarma algoritmalarının bilgisayar destekli yöntemlere uyarlanması kesin bir ihtiyaçtır. Üretken temsili öğrenme algoritmaları, Derin Öğrenmenin avantajları ile son yıllarda ortaya çıkan yaklaşımlardır. Bu çalışmada, ROI görüntülerinde meme kanseri teşhisi için Derin İnanç Ağlarının (DBN) kullanılmasını önerdim. DBN modelleri, boyutun ROI görüntüleri üzerindeki etkisini değerlendirmek için farklı görüntü boyutları üzerinde tekrarlanmıştır. Önerilen DBN modeli doğruluk, özgüllük, duyarlılık ve kesinlik için sırasıyla %96.32, %96.68, %95.93 ve %96.40 performans oranlarına ulaşmıştır. Sonuç olarak, önerilen ayrıntılı temsili öğrenmeye sahip DBN, üretici yapıların avantajı ile meme kanseri ve sağlıklı dokuların mamogramlarda sınıflandırılması için verimli ve sağlam bir prosedürdür.

References

  • [1] Yoon S, Kim S. "AdaBoost-Based multiple SVM-RFE for classification of mammograms in DDSM". IEEE 2008 International Conference on Bioinformatics and Biomedicine Workshops, Philadelphia, PA, USA, 3-5 November 2008.
  • [2] Abdelrahman L, Al Ghamdi M, Collado-Mesa F, AbdelMottaleb M. "Convolutional neural networks for breast cancer detection in mammography: A survey". Computers in Biology and Medicine, 2021. https://doi.org/10.1016/j.compbiomed.2021.104248.
  • [3] Al-antari MA, Al-masni MA, Park SU, Park JH, Metwally MK, Kadah YM, Han SM, Kim TS. "An automatic computeraided diagnosis system for breast cancer in digital mammograms via deep belief network". Journal of Medical and Biological Engineering, 38(3), 443-456, 2018.
  • [4] Nasir Khan H, Shahid AR, Raza B, Dar AH, Alquhayz H. "Multi-View feature fusion based four views model for mammogram classification using convolutional neural network". IEEE Access, 7, 165724-165733, 2019.
  • [5] Alanazi SA, Kamruzzaman MM, Islam Sarker MN, Alruwaili M, Alhwaiti Y, Alshammari N, Siddiqi MH. "Boosting Breast Cancer Detection Using Convolutional Neural Network". Journal of Healthcare Engineering, 2021. https://doi.org/10.1155/2021/5528622.
  • [6] Le Roux N, Bengio Y. "Representational power of restricted boltzmann machines and deep belief networks". Neural computation, 20(6), 1631-1649. 2008.
  • [7] Pardamean B, Cenggoro TW, Rahutomo R, Budiarto A, Karuppiah EK. "Transfer learning from chest x-ray pre-trained convolutional neural network for learning mammogram data". Procedia Computer Science, 135, 400-407, 2018.
  • [8] Zeng Q, Jiang H, Ma L. "Learning multi-level features for breast mass detection". ACM 2nd International Symposium on Image Computing and Digital Medicine (ISICDM2018), Chengdu, China, 13 October 2018.
  • [9] Yu X, Zeng N, Liu S, Zhang Y-D. "Utilization of DenseNet201 for diagnosis of breast abnormality". Machine Vision and Applications, 30(7), 1135-1144, 2019.
  • [10] Ertosun MG, Rubin DL. "Probabilistic visual search for masses within mammography images using deep learning". IEEE 2015 International Conference on Bioinformatics and Biomedicine (BIBM), Washington, DC, USA, 9-12 November 2015.
  • [11] Xi P, Shu C, Goubran R. "Abnormality detection in mammography using deep convolutional neural networks". IEEE 2018 International Symposium on Medical Measurements and Applications (MeMeA), Rome, Italy, 11-13 June 2018.
  • [12] Agarwal R, Diaz O, Marti R, Llado X. "Mass detection in mammograms using pre-trained deep learning models". In: Krupinski EA, editor. SPIE 2018 14th International Workshop on Breast Imaging (IWBI 2018), Atlanta, Georgia, United States, 6 July 2018.
  • [13] Suzuki S, Zhang X, Homma N, Ichiji K, Sugita N, Kawasumi Y, Ishibashi T, Yoshizawa M. "Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis". IEEE 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). Tsukuba, Japan, 20-23 September 2016.
  • [14] Touahri R, AzizI N, Hammami NE, Aldwairi M, Benaida F. "Automated breast tumor diagnosis using local binary patterns (LBP) based on deep learning classification". IEEE 2019 International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia, 3-4 April 2019.
  • [15] Swiderski B, Kurek J, Osowski S, Kruk M, Barhoumi W. "Deep learning and non-negative matrix factorization in recognition of mammograms". SPIE 2016 Eighth International Conference on Graphic and Image Processing (ICGIP 2016), Tokyo, Japan, 8 February 2017.
  • [16] Nguyen VD, Lim K, Le MD, Dung Bui N. "Combination of gabor filter and convolutional neural network for suspicious mass classification". IEEE 2018 22nd International Computer Science and Engineering Conference (ICSEC). Chiang Mai, Thailand, 21-24 November 2018.
  • [17] Eleyan G, Salman M. "Image denoising with twodimensional zero attracting LMS algorithm". Pamukkale University Jornal of Engineering Sciences, 25(5), 539-45, 2019.
  • [18] Sümer E, Engin M, Ağıldere M, Oğul H. "Monitoring nodule progression in chest X-ray images". Pamukkale University Jornal of Engineering Sciences, 24(5), 934-41, 2018.
  • [19] Yıldız K, Demir Ö, Ülkü EE. "Fault detection of fabrics using image processing methods". Pamukkale University Jornal of Engineering Sciences, 23(7), 841-844, 2017.
  • [20] Hekal AA, Elnakib A, Moustafa HE-D. "Automated early breast cancer detection and classification system". Signal, Image, and Video Processing, 15, 1497-1505, 2021. 2021.
  • [21] Sarosa SJA, Utaminingrum F, Bachtiar FA. "Mammogram breast cancer classification using gray-level co-occurrence matrix and support vector machine". IEEE 2018 3rd International Conference on Sustainable Information Engineering and Technology (SIET 2018), Malang, Indonesia, 10-12 November 2018.
  • [22] Hinton GE, Osindero S, Teh YW. "A fast learning algorithm for deep belief nets". Neural Computation, 18(7), 1527-1554, 2006.
  • [23] Nair V, Hinton GE. "3D object recognition with deep belief nets". Advances in Neural Information Processing Systems, 22, 1339-1347, 2009.
  • [24] Khatami A, Khosravi A, Nguyen T, Lim CP, Nahavandi S. "Medical image analysis using wavelet transform and deep belief networks". Expert Systems with Applications, 86, 190-198, 2017.
  • [25] Altan G, Kutlu Y, Allahverdi N. "A multistage deep belief networks application on arrhythmia classification". International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue-1), 222-228, 2016.
  • [26] Altan G, Allahverdi N, Kutlu Y. "Diagnosis of coronary artery disease using deep belief networks". European journal of engineering and natural sciences, 2(1), 29-36, 2017.
  • [27] Abdel-Zaher AM, Eldeib AM. "Breast cancer classification using deep belief networks". Expert Systems with Applications, 46, 139-144, 2016.
  • [28] Heath M, Bowyer K, Kopans RM. Current Status of the Digital Database for Screening Mammography. Editors: Karssemeijer N, Thijssen M, Hendriks J, van Erning L. Digital Mammography. Computational Imaging and Vision, 457-460, Dordrecht, Springer, 1998.
  • [29] Hinton G. "Deep belief networks". Scholarpedia, 2009. doi:10.4249/scholarpedia.5947.
  • [30] Altan G. "SecureDeepNet‐IoT: A deep learning application for invasion detection in industrial Internet of things sensing systems". Transactions on Emerging Telecommunications Technologies, 2021. https://doi.org/10.1002/ett.4228.
  • [31] Altan G. A Deep Learning Architecture for Identification of Breast Cancer on Mammography by Learning Various Representations of Cancerous Mass. Editors: Kose U and Alzubi J. Deep Learning for Cancer Diagnosis, 169-187, Singapore, Springer, 2021.

Breast cancer diagnosis using deep belief networks on ROI images

Year 2022, Volume: 28 Issue: 2, 286 - 291, 30.04.2022

Abstract

Hand-crafted features are efficient methods for image processing, recognition, and computer vision. However, the advancements in data size and image resolution lead to inconvenience in feature extraction. Moreover, they are unstable, method-dependent, and computationally intensive due to high dimensions. Especially, big data on image datasets causes unpredictable long process. It is a definite necessity to adjust the feature extraction algorithms to computer-assisted methods for image processing. Generative representational learning algorithms have been emerging approaches with the advantages of Deep Learning. In this study, I proposed employing Deep Belief Networks (DBN) for breast cancer diagnosis on ROI images. DBN models were iterated on different image sizes to evaluate the impact of dimensionality on ROI images. The proposed DBN model has achieved performance rates of 96.32%, 96.68%, 95.93%, and 96.40% for accuracy, specificity, sensitivity, and precision, respectively. Consequently, the proposed DBN with detailed representational learning is an efficient and robust algorithm for the classification of breast cancer and healthy tissues on mammograms by the advantage of generative architectures.

References

  • [1] Yoon S, Kim S. "AdaBoost-Based multiple SVM-RFE for classification of mammograms in DDSM". IEEE 2008 International Conference on Bioinformatics and Biomedicine Workshops, Philadelphia, PA, USA, 3-5 November 2008.
  • [2] Abdelrahman L, Al Ghamdi M, Collado-Mesa F, AbdelMottaleb M. "Convolutional neural networks for breast cancer detection in mammography: A survey". Computers in Biology and Medicine, 2021. https://doi.org/10.1016/j.compbiomed.2021.104248.
  • [3] Al-antari MA, Al-masni MA, Park SU, Park JH, Metwally MK, Kadah YM, Han SM, Kim TS. "An automatic computeraided diagnosis system for breast cancer in digital mammograms via deep belief network". Journal of Medical and Biological Engineering, 38(3), 443-456, 2018.
  • [4] Nasir Khan H, Shahid AR, Raza B, Dar AH, Alquhayz H. "Multi-View feature fusion based four views model for mammogram classification using convolutional neural network". IEEE Access, 7, 165724-165733, 2019.
  • [5] Alanazi SA, Kamruzzaman MM, Islam Sarker MN, Alruwaili M, Alhwaiti Y, Alshammari N, Siddiqi MH. "Boosting Breast Cancer Detection Using Convolutional Neural Network". Journal of Healthcare Engineering, 2021. https://doi.org/10.1155/2021/5528622.
  • [6] Le Roux N, Bengio Y. "Representational power of restricted boltzmann machines and deep belief networks". Neural computation, 20(6), 1631-1649. 2008.
  • [7] Pardamean B, Cenggoro TW, Rahutomo R, Budiarto A, Karuppiah EK. "Transfer learning from chest x-ray pre-trained convolutional neural network for learning mammogram data". Procedia Computer Science, 135, 400-407, 2018.
  • [8] Zeng Q, Jiang H, Ma L. "Learning multi-level features for breast mass detection". ACM 2nd International Symposium on Image Computing and Digital Medicine (ISICDM2018), Chengdu, China, 13 October 2018.
  • [9] Yu X, Zeng N, Liu S, Zhang Y-D. "Utilization of DenseNet201 for diagnosis of breast abnormality". Machine Vision and Applications, 30(7), 1135-1144, 2019.
  • [10] Ertosun MG, Rubin DL. "Probabilistic visual search for masses within mammography images using deep learning". IEEE 2015 International Conference on Bioinformatics and Biomedicine (BIBM), Washington, DC, USA, 9-12 November 2015.
  • [11] Xi P, Shu C, Goubran R. "Abnormality detection in mammography using deep convolutional neural networks". IEEE 2018 International Symposium on Medical Measurements and Applications (MeMeA), Rome, Italy, 11-13 June 2018.
  • [12] Agarwal R, Diaz O, Marti R, Llado X. "Mass detection in mammograms using pre-trained deep learning models". In: Krupinski EA, editor. SPIE 2018 14th International Workshop on Breast Imaging (IWBI 2018), Atlanta, Georgia, United States, 6 July 2018.
  • [13] Suzuki S, Zhang X, Homma N, Ichiji K, Sugita N, Kawasumi Y, Ishibashi T, Yoshizawa M. "Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis". IEEE 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). Tsukuba, Japan, 20-23 September 2016.
  • [14] Touahri R, AzizI N, Hammami NE, Aldwairi M, Benaida F. "Automated breast tumor diagnosis using local binary patterns (LBP) based on deep learning classification". IEEE 2019 International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia, 3-4 April 2019.
  • [15] Swiderski B, Kurek J, Osowski S, Kruk M, Barhoumi W. "Deep learning and non-negative matrix factorization in recognition of mammograms". SPIE 2016 Eighth International Conference on Graphic and Image Processing (ICGIP 2016), Tokyo, Japan, 8 February 2017.
  • [16] Nguyen VD, Lim K, Le MD, Dung Bui N. "Combination of gabor filter and convolutional neural network for suspicious mass classification". IEEE 2018 22nd International Computer Science and Engineering Conference (ICSEC). Chiang Mai, Thailand, 21-24 November 2018.
  • [17] Eleyan G, Salman M. "Image denoising with twodimensional zero attracting LMS algorithm". Pamukkale University Jornal of Engineering Sciences, 25(5), 539-45, 2019.
  • [18] Sümer E, Engin M, Ağıldere M, Oğul H. "Monitoring nodule progression in chest X-ray images". Pamukkale University Jornal of Engineering Sciences, 24(5), 934-41, 2018.
  • [19] Yıldız K, Demir Ö, Ülkü EE. "Fault detection of fabrics using image processing methods". Pamukkale University Jornal of Engineering Sciences, 23(7), 841-844, 2017.
  • [20] Hekal AA, Elnakib A, Moustafa HE-D. "Automated early breast cancer detection and classification system". Signal, Image, and Video Processing, 15, 1497-1505, 2021. 2021.
  • [21] Sarosa SJA, Utaminingrum F, Bachtiar FA. "Mammogram breast cancer classification using gray-level co-occurrence matrix and support vector machine". IEEE 2018 3rd International Conference on Sustainable Information Engineering and Technology (SIET 2018), Malang, Indonesia, 10-12 November 2018.
  • [22] Hinton GE, Osindero S, Teh YW. "A fast learning algorithm for deep belief nets". Neural Computation, 18(7), 1527-1554, 2006.
  • [23] Nair V, Hinton GE. "3D object recognition with deep belief nets". Advances in Neural Information Processing Systems, 22, 1339-1347, 2009.
  • [24] Khatami A, Khosravi A, Nguyen T, Lim CP, Nahavandi S. "Medical image analysis using wavelet transform and deep belief networks". Expert Systems with Applications, 86, 190-198, 2017.
  • [25] Altan G, Kutlu Y, Allahverdi N. "A multistage deep belief networks application on arrhythmia classification". International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue-1), 222-228, 2016.
  • [26] Altan G, Allahverdi N, Kutlu Y. "Diagnosis of coronary artery disease using deep belief networks". European journal of engineering and natural sciences, 2(1), 29-36, 2017.
  • [27] Abdel-Zaher AM, Eldeib AM. "Breast cancer classification using deep belief networks". Expert Systems with Applications, 46, 139-144, 2016.
  • [28] Heath M, Bowyer K, Kopans RM. Current Status of the Digital Database for Screening Mammography. Editors: Karssemeijer N, Thijssen M, Hendriks J, van Erning L. Digital Mammography. Computational Imaging and Vision, 457-460, Dordrecht, Springer, 1998.
  • [29] Hinton G. "Deep belief networks". Scholarpedia, 2009. doi:10.4249/scholarpedia.5947.
  • [30] Altan G. "SecureDeepNet‐IoT: A deep learning application for invasion detection in industrial Internet of things sensing systems". Transactions on Emerging Telecommunications Technologies, 2021. https://doi.org/10.1002/ett.4228.
  • [31] Altan G. A Deep Learning Architecture for Identification of Breast Cancer on Mammography by Learning Various Representations of Cancerous Mass. Editors: Kose U and Alzubi J. Deep Learning for Cancer Diagnosis, 169-187, Singapore, Springer, 2021.
There are 31 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Elektrik Elektornik Müh. / Bilgisayar Müh.
Authors

Gökhan Altan This is me

Publication Date April 30, 2022
Published in Issue Year 2022 Volume: 28 Issue: 2

Cite

APA Altan, G. (2022). Breast cancer diagnosis using deep belief networks on ROI images. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 28(2), 286-291.
AMA Altan G. Breast cancer diagnosis using deep belief networks on ROI images. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. April 2022;28(2):286-291.
Chicago Altan, Gökhan. “Breast Cancer Diagnosis Using Deep Belief Networks on ROI Images”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 28, no. 2 (April 2022): 286-91.
EndNote Altan G (April 1, 2022) Breast cancer diagnosis using deep belief networks on ROI images. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 28 2 286–291.
IEEE G. Altan, “Breast cancer diagnosis using deep belief networks on ROI images”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 28, no. 2, pp. 286–291, 2022.
ISNAD Altan, Gökhan. “Breast Cancer Diagnosis Using Deep Belief Networks on ROI Images”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 28/2 (April 2022), 286-291.
JAMA Altan G. Breast cancer diagnosis using deep belief networks on ROI images. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2022;28:286–291.
MLA Altan, Gökhan. “Breast Cancer Diagnosis Using Deep Belief Networks on ROI Images”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 28, no. 2, 2022, pp. 286-91.
Vancouver Altan G. Breast cancer diagnosis using deep belief networks on ROI images. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2022;28(2):286-91.

ESCI_LOGO.png    image001.gif    image002.gif        image003.gif     image004.gif