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SimCLR-based Self-Supervised Learning Approach for Limited Brain MRI and Unlabeled Images

Yıl 2024, Cilt: 13 Sayı: 4, 1304 - 1313, 31.12.2024

Kazım Fırıldak , Gaffari Çelik , Muhammed Fatih Talu

https://doi.org/10.17798/bitlisfen.1558069

Öz

In this study, a SimCLR-based model is proposed for the classification of unlabeled brain tumor images in medical imaging using a self-supervised learning (SSL) technique. Additionally, the performances of different SSL techniques (Barlow Twins, NnCLR, and SimCLR) are analyzed to evaluate the performance of the proposed model. Three different datasets, consisting of pituitary, meningioma, and glioma brain tumors as well as non-tumor images, were used as the dataset. Out of a total of 7,671 images, 6,128 were used as unlabeled data, and the model was trained with both labeled and unlabeled data. The proposed model achieved high performance with unlabeled data, reducing the need for manual labeling. As a result, the model demonstrated superior performance compared to other models, with high performance values such as 99.35% c_acc and 96.31% p_acc.

Anahtar Kelimeler

Unlabeled images Brain tumor Self-Supervised Learning SimCLR Barlow Twins NnCLR

Etik Beyan

The study is complied with research and publication ethics.

Kaynakça

  • [1] M. Toğaçar, N. Muzoğlu, B. Ergen, B. S. B. Yarman, and A. M. Halefoğlu, “Detection of COVID-19 findings by the local interpretable model-agnostic explanations method of types-based activations extracted from CNNs,” Biomed. Signal Process. Control, vol. 71, p. 103128, Jan. 2022, doi: 10.1016/j.bspc.2021.103128.
  • [2] G. Celik, “Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network,” Appl. Soft Comput., vol. 133, p. 109906, Jan. 2023, doi: 10.1016/j.asoc.2022.109906.
  • [3] E. Başaran, “A new brain tumor diagnostic model: Selection of textural feature extraction algorithms and convolution neural network features with optimization algorithms,” Comput. Biol. Med., vol. 148, p. 105857, Sep. 2022, doi: 10.1016/j.compbiomed.2022.105857.
  • [4] G. Çelik and M. F. Talu, “A new 3D MRI segmentation method based on Generative Adversarial Network and Atrous Convolution,” Biomed. Signal Process. Control, vol. 71, p. 103155, Jan. 2022, doi: 10.1016/j.bspc.2021.103155.
  • [5] S. Altun Güven and M. F. Talu, “Brain MRI high resolution image creation and segmentation with the new GAN method,” Biomed. Signal Process. Control, vol. 80, p. 104246, Feb. 2023, doi: 10.1016/j.bspc.2022.104246.
  • [6] Z. Bozdag and M. F. Talu, “Pyramidal position attention model for histopathological image segmentation,” Biomed. Signal Process. Control, vol. 80, p. 104374, Feb. 2023, doi: 10.1016/j.bspc.2022.104374.
  • [7] G. Celik and E. Başaran, “Proposing a new approach based on convolutional neural networks and random forest for the diagnosis of Parkinson’s disease from speech signals,” Appl. Acoust., vol. 211, p. 109476, Aug. 2023, doi: 10.1016/j.apacoust.2023.109476.
  • [8] S. Mavaddati, “Voice-based age, gender, and language recognition based on ResNet deep model and transfer learning in spectro-temporal domain,” Neurocomputing, vol. 580, p. 127429, May 2024, doi: 10.1016/j.neucom.2024.127429.
  • [9] M. A. Islam, M. Z. Hasan Majumder, M. A. Hussein, K. M. Hossain, and M. S. Miah, “A review of machine learning and deep learning algorithms for Parkinson’s disease detection using handwriting and voice datasets,” Heliyon, vol. 10, no. 3, p. e25469, Feb. 2024, doi: 10.1016/j.heliyon.2024.e25469.
  • [10] R. B. Rahman, S. A. Tanim, N. Alfaz, T. E. Shrestha, M. S. U. Miah, and M. F. Mridha, “A comprehensive dental dataset of six classes for deep learning based object detection study,” Data Br., vol. 57, p. 110970, Dec. 2024, doi: 10.1016/j.dib.2024.110970.
  • [11] B. Ganga, L. B.T., and V. K.R., “Object detection and crowd analysis using deep learning techniques: Comprehensive review and future directions,” Neurocomputing, vol. 597, p. 127932, Sep. 2024, doi: 10.1016/j.neucom.2024.127932.
  • [12] K. Kantor and M. Morzy, “Machine learning and natural language processing in clinical trial eligibility criteria parsing: a scoping review,” Drug Discov. Today, vol. 29, no. 10, p. 104139, Oct. 2024, doi: 10.1016/j.drudis.2024.104139.
  • [13] A. Montejo-Ráez, M. D. Molina-González, S. M. Jiménez-Zafra, M. Á. García-Cumbreras, and L. J. García-López, “A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges,” Comput. Sci. Rev., vol. 53, p. 100654, Aug. 2024, doi: 10.1016/j.cosrev.2024.100654.
  • [14] L. Chen, P. Bentley, K. Mori, K. Misawa, M. Fujiwara, and D. Rueckert, “Self-supervised learning for medical image analysis using image context restoration,” Med. Image Anal., vol. 58, p. 101539, Dec. 2019, doi: 10.1016/j.media.2019.101539.
  • [15] A. Jaiswal, A. R. Babu, M. Z. Zadeh, D. Banerjee, and F. Makedon, “A Survey on Contrastive Self-Supervised Learning,” Technologies, vol. 9, no. 1, p. 2, Dec. 2020, doi: 10.3390/technologies9010002.
  • [16] X. Liu et al., “Self-supervised Learning: Generative or Contrastive,” IEEE Trans. Knowl. Data Eng., pp. 1–1, 2021, doi: 10.1109/TKDE.2021.3090866.
  • [17] I. Misra and L. van der Maaten, “Self-Supervised Learning of Pretext-Invariant Representations,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Jun. 2020, pp. 6706–6716. doi: 10.1109/CVPR42600.2020.00674.
  • [18] Y. Wang, C. M. Albrecht, N. A. A. Braham, L. Mou, and X. X. Zhu, “Self-Supervised Learning in Remote Sensing: A review,” IEEE Geosci. Remote Sens. Mag., vol. 10, no. 4, pp. 213–247, Dec. 2022, doi: 10.1109/MGRS.2022.3198244.
  • [19] S. Shurrab and R. Duwairi, “Self-supervised learning methods and applications in medical imaging analysis: a survey,” PeerJ Comput. Sci., vol. 8, p. e1045, Jul. 2022, doi: 10.7717/peerj-cs.1045.
  • [20] V. R. de Sa, “Learning Classification with Unlabeled Data,” Adv. Neural Inf. Process. Syst., pp. 112–119, 1994, [Online]. Available: https://dl.acm.org/doi/10.5555/2987189.2987204
  • [21] S. Gupta, “Brain MRI Scans for brain tumor classification.” Accessed: Jan. 25, 2024. [Online]. Available: https://www.kaggle.com/datasets/shreyag1103/brain-mri-scans-for-brain-tumor-classification
  • [22] S. Bhuvaji, A. Kadam, P. Bhumkar, S. Dedge, and S. Kanchan, “Brain Tumor Classification (MRI).” Accessed: Jan. 12, 2024. [Online]. Available: https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri/data
  • [23] Thomas, “Brain tumors.” Accessed: Jan. 18, 2024. [Online]. Available: https://www.kaggle.com/datasets/thomasdubail/brain-tumors-256x256?select=Data
  • [24] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A Simple Framework for Contrastive Learning of Visual Representations,” Feb. 2020, [Online]. Available: http://arxiv.org/abs/2002.05709
  • [25] J. Zbontar, L. Jing, I. Misra, Y. LeCun, and S. Deny, “Barlow Twins: Self-Supervised Learning via Redundancy Reduction,” Mar. 2021, [Online]. Available: http://arxiv.org/abs/2103.03230
  • [26] D. Dwibedi, Y. Aytar, J. Tompson, P. Sermanet, and A. Zisserman, “With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations,” Apr. 2021, [Online]. Available: http://arxiv.org/abs/2104.14548
  • [27] A. van den Oord, Y. Li, and O. Vinyals, “Representation Learning with Contrastive Predictive Coding,” Jul. 2018.
  • [28] J. C. Triana-Martinez, J. Gil-González, J. A. Fernandez-Gallego, A. M. Álvarez-Meza, and C. G. Castellanos-Dominguez, “Chained Deep Learning Using Generalized Cross-Entropy for Multiple Annotators Classification,” Sensors, vol. 23, no. 7, p. 3518, Mar. 2023, doi: 10.3390/s23073518.

Yıl 2024, Cilt: 13 Sayı: 4, 1304 - 1313, 31.12.2024

Kazım Fırıldak , Gaffari Çelik , Muhammed Fatih Talu

https://doi.org/10.17798/bitlisfen.1558069

Öz

Kaynakça

  • [1] M. Toğaçar, N. Muzoğlu, B. Ergen, B. S. B. Yarman, and A. M. Halefoğlu, “Detection of COVID-19 findings by the local interpretable model-agnostic explanations method of types-based activations extracted from CNNs,” Biomed. Signal Process. Control, vol. 71, p. 103128, Jan. 2022, doi: 10.1016/j.bspc.2021.103128.
  • [2] G. Celik, “Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network,” Appl. Soft Comput., vol. 133, p. 109906, Jan. 2023, doi: 10.1016/j.asoc.2022.109906.
  • [3] E. Başaran, “A new brain tumor diagnostic model: Selection of textural feature extraction algorithms and convolution neural network features with optimization algorithms,” Comput. Biol. Med., vol. 148, p. 105857, Sep. 2022, doi: 10.1016/j.compbiomed.2022.105857.
  • [4] G. Çelik and M. F. Talu, “A new 3D MRI segmentation method based on Generative Adversarial Network and Atrous Convolution,” Biomed. Signal Process. Control, vol. 71, p. 103155, Jan. 2022, doi: 10.1016/j.bspc.2021.103155.
  • [5] S. Altun Güven and M. F. Talu, “Brain MRI high resolution image creation and segmentation with the new GAN method,” Biomed. Signal Process. Control, vol. 80, p. 104246, Feb. 2023, doi: 10.1016/j.bspc.2022.104246.
  • [6] Z. Bozdag and M. F. Talu, “Pyramidal position attention model for histopathological image segmentation,” Biomed. Signal Process. Control, vol. 80, p. 104374, Feb. 2023, doi: 10.1016/j.bspc.2022.104374.
  • [7] G. Celik and E. Başaran, “Proposing a new approach based on convolutional neural networks and random forest for the diagnosis of Parkinson’s disease from speech signals,” Appl. Acoust., vol. 211, p. 109476, Aug. 2023, doi: 10.1016/j.apacoust.2023.109476.
  • [8] S. Mavaddati, “Voice-based age, gender, and language recognition based on ResNet deep model and transfer learning in spectro-temporal domain,” Neurocomputing, vol. 580, p. 127429, May 2024, doi: 10.1016/j.neucom.2024.127429.
  • [9] M. A. Islam, M. Z. Hasan Majumder, M. A. Hussein, K. M. Hossain, and M. S. Miah, “A review of machine learning and deep learning algorithms for Parkinson’s disease detection using handwriting and voice datasets,” Heliyon, vol. 10, no. 3, p. e25469, Feb. 2024, doi: 10.1016/j.heliyon.2024.e25469.
  • [10] R. B. Rahman, S. A. Tanim, N. Alfaz, T. E. Shrestha, M. S. U. Miah, and M. F. Mridha, “A comprehensive dental dataset of six classes for deep learning based object detection study,” Data Br., vol. 57, p. 110970, Dec. 2024, doi: 10.1016/j.dib.2024.110970.
  • [11] B. Ganga, L. B.T., and V. K.R., “Object detection and crowd analysis using deep learning techniques: Comprehensive review and future directions,” Neurocomputing, vol. 597, p. 127932, Sep. 2024, doi: 10.1016/j.neucom.2024.127932.
  • [12] K. Kantor and M. Morzy, “Machine learning and natural language processing in clinical trial eligibility criteria parsing: a scoping review,” Drug Discov. Today, vol. 29, no. 10, p. 104139, Oct. 2024, doi: 10.1016/j.drudis.2024.104139.
  • [13] A. Montejo-Ráez, M. D. Molina-González, S. M. Jiménez-Zafra, M. Á. García-Cumbreras, and L. J. García-López, “A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges,” Comput. Sci. Rev., vol. 53, p. 100654, Aug. 2024, doi: 10.1016/j.cosrev.2024.100654.
  • [14] L. Chen, P. Bentley, K. Mori, K. Misawa, M. Fujiwara, and D. Rueckert, “Self-supervised learning for medical image analysis using image context restoration,” Med. Image Anal., vol. 58, p. 101539, Dec. 2019, doi: 10.1016/j.media.2019.101539.
  • [15] A. Jaiswal, A. R. Babu, M. Z. Zadeh, D. Banerjee, and F. Makedon, “A Survey on Contrastive Self-Supervised Learning,” Technologies, vol. 9, no. 1, p. 2, Dec. 2020, doi: 10.3390/technologies9010002.
  • [16] X. Liu et al., “Self-supervised Learning: Generative or Contrastive,” IEEE Trans. Knowl. Data Eng., pp. 1–1, 2021, doi: 10.1109/TKDE.2021.3090866.
  • [17] I. Misra and L. van der Maaten, “Self-Supervised Learning of Pretext-Invariant Representations,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Jun. 2020, pp. 6706–6716. doi: 10.1109/CVPR42600.2020.00674.
  • [18] Y. Wang, C. M. Albrecht, N. A. A. Braham, L. Mou, and X. X. Zhu, “Self-Supervised Learning in Remote Sensing: A review,” IEEE Geosci. Remote Sens. Mag., vol. 10, no. 4, pp. 213–247, Dec. 2022, doi: 10.1109/MGRS.2022.3198244.
  • [19] S. Shurrab and R. Duwairi, “Self-supervised learning methods and applications in medical imaging analysis: a survey,” PeerJ Comput. Sci., vol. 8, p. e1045, Jul. 2022, doi: 10.7717/peerj-cs.1045.
  • [20] V. R. de Sa, “Learning Classification with Unlabeled Data,” Adv. Neural Inf. Process. Syst., pp. 112–119, 1994, [Online]. Available: https://dl.acm.org/doi/10.5555/2987189.2987204
  • [21] S. Gupta, “Brain MRI Scans for brain tumor classification.” Accessed: Jan. 25, 2024. [Online]. Available: https://www.kaggle.com/datasets/shreyag1103/brain-mri-scans-for-brain-tumor-classification
  • [22] S. Bhuvaji, A. Kadam, P. Bhumkar, S. Dedge, and S. Kanchan, “Brain Tumor Classification (MRI).” Accessed: Jan. 12, 2024. [Online]. Available: https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri/data
  • [23] Thomas, “Brain tumors.” Accessed: Jan. 18, 2024. [Online]. Available: https://www.kaggle.com/datasets/thomasdubail/brain-tumors-256x256?select=Data
  • [24] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A Simple Framework for Contrastive Learning of Visual Representations,” Feb. 2020, [Online]. Available: http://arxiv.org/abs/2002.05709
  • [25] J. Zbontar, L. Jing, I. Misra, Y. LeCun, and S. Deny, “Barlow Twins: Self-Supervised Learning via Redundancy Reduction,” Mar. 2021, [Online]. Available: http://arxiv.org/abs/2103.03230
  • [26] D. Dwibedi, Y. Aytar, J. Tompson, P. Sermanet, and A. Zisserman, “With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations,” Apr. 2021, [Online]. Available: http://arxiv.org/abs/2104.14548
  • [27] A. van den Oord, Y. Li, and O. Vinyals, “Representation Learning with Contrastive Predictive Coding,” Jul. 2018.
  • [28] J. C. Triana-Martinez, J. Gil-González, J. A. Fernandez-Gallego, A. M. Álvarez-Meza, and C. G. Castellanos-Dominguez, “Chained Deep Learning Using Generalized Cross-Entropy for Multiple Annotators Classification,” Sensors, vol. 23, no. 7, p. 3518, Mar. 2023, doi: 10.3390/s23073518.
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Kazım Fırıldak 0000-0002-1958-3627

Gaffari Çelik 0000-0001-5658-9529

Muhammed Fatih Talu 0000-0003-1166-8404

Erken Görünüm Tarihi 30 Aralık 2024
Yayımlanma Tarihi 31 Aralık 2024
Gönderilme Tarihi 29 Eylül 2024
Kabul Tarihi 28 Ekim 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 13 Sayı: 4

Kaynak Göster

IEEE K. Fırıldak, G. Çelik, ve M. F. Talu, “SimCLR-based Self-Supervised Learning Approach for Limited Brain MRI and Unlabeled Images”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, c. 13, sy. 4, ss. 1304–1313, 2024, doi: 10.17798/bitlisfen.1558069.
 Kapak Resmi İndir

Bitlis Eren University
Journal of Science Editor
Bitlis Eren University Graduate Institute
Bes Minare Mah. Ahmet Eren Bulvari, Merkez Kampus, 13000 BITLIS