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Öz Dikkat Mekanizması Tabanlı Görü Dönüştürücü Kullanılarak Sıtma Parazit Tespiti

Year 2022, Volume: 13 Issue: 2, 271 - 277, 28.06.2022
https://doi.org/10.24012/dumf.1120289

Abstract

Sıtma, tedavisiz olgularda ölümle sonuçlanabilen ve ciddi ateşli hastalığa yol açan bir enfeksiyon hastalığıdır. Bu yüzden bu hastalığın erken tanı ve tedavisi oldukça kritik öneme sahiptir. Gelişmiş teknolojilerle birlikte sıtma hastalığının teşhisine yönelik birçok klinik yöntem ve test kullanılmaktadır. Bu çalışmada Sıtma hastalığının teşhis edilmesi amacıyla son yıllarda doğal dil işleme alanında oldukça yüksek performans gösteren dönüştürücü yöntemlerden esinlenilerek önerilen Görü Dönüştürücü (Vision Transformer, ViT) yöntemi kullanılmaktadır. Elde edilen sonuçlar değerlendirildiğinde ViT yönteminin %97.22 gibi yüksek bir sınıflandırma performansı elde ettiği gözlemlenmiştir. Vit yöntemi ile elde edilen sonuçlar, literatürde uygulanan geleneksel ve derin öğrenme yöntemleri karşılaştırılmış ve bu sonuçlar karşılaştırmalı olarak tabloda sunulmuştur.

References

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  • [4] D. Bibin, M. S. Nair and P. Punitha, "Malaria Parasite Detection From Peripheral Blood Smear Images Using Deep Belief Networks," in IEEE Access, vol. 5, pp. 9099-9108, 2017, doi: 10.1109/ACCESS.2017.2705642.
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  • [6] J. Hung and A. Carpenter, "Applying Faster R-CNN for Object Detection on Malaria Images," 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017, pp. 808-813, doi: 10.1109/CVPRW.2017.112.
  • [7] B. N. Narayanan, R. Ali and R. C. Hardie, "Performance analysis of machine learning and deep learning architectures for malaria detection on cell images", Applications of Machine Learning, vol. 11139, pp. 111390W, September 2019.
  • [8] S. Muneer, S. Jatoi, B. Naz, “Detection of Noisy Blood Images Indicating Prodromal Diseases”, International Journal of Latest Trends in Engineering and Technology, Vol(12), Issue(6), pp. 001-006, 2018.
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  • [13] Q. Wang et al., "Learning deep transformer models for machine translation", Proc. Annu. Meeting Assoc. Comput. Linguistics, pp. 1810-1822, 2019.
  • [14] T. Czempiel, M. Paschali, D. Ostler, S. T. Kim, B. Busam and N. Navab, "OperA: Attention-regularized transformers for surgical phase recognition", arXiv:2103.03873, 2021, [online] Available: http://arxiv.org/abs/2103.03873.
  • [15] A. Diker, "Sıtma Hastalığının Sınıflandırılmasında Evrişimsel Sinir Ağlarının Performanslarının Karşılaştırılması", BEÜ Fen Bilim. Derg., vol. 9, no. 4, pp. 1825-1835, 2020.
  • [16] M. Masud, H. Alhumyani, S. S. Alshamrani, O. Cheikhrouhou, S. Ibrahim, G. Muhammad, et al., "Leveraging deep learning techniques for malaria parasite detection using mobile application", Wireless Commun. Mobile Comput., vol. 2020, pp. 1-15, Jul. 2020.
  • [17] T. Mahmud, & S. A. Fattah, “Automatic Diagnosis of Malaria from Thin Blood Smear Images using Deep Convolutional Neural Network with Multi-Resolution Feature Fusion”, arXiv preprint arXiv:2012.05350. 2020.
  • [18] B. N. Akılotu, Z. Kadiroğlu, A. Şengür and M. Kayaoğlu, “Evrişimsel Sinir Ağları ve Transfer Öğrenme Yöntemi Kullanılarak Sıtma Tespiti”, International Engineering and Science Symposium, Siirt, 2019.
  • [19] Whole slide image for malaria infected red blood cells, [Çevrimiçi] Erişim: http://peir-vm.path.uab.edu/debug.php?slide=IPLab11Malaria [Erişim Tarihi: 15.05.2021].
  • [20] F. Montalbo and A. Alon, "Empirical Analysis of a Fine-Tuned Deep Convolutional Model in Classifying and Detecting Malaria Parasites from Blood Smears", KSII Transactions on Internet and Information Systems, vol. 15, no. 1, 2021.
  • [21] E. Irmak, “A novel implementation of deep-learning approach on malaria parasite detection from thin blood cell images”, Electrica 21(2):216–224. 2021. https://doi.org/10.5152/electrica.2020.21004.
  • [22] T. Fatima and M.S Farid, "Automatic detection of Plasmodium parasites from microscopic blood images", Journal of Parasitic Diseases, vol. 44, no. 1, pp. 69-78, 2020.
  • [23] G. Saiprasath, N. Babu, J. ArunPriyan, R. Vinayakumar, V. Sowmya and K. Soman, Performance comparison of machine learning algorithms for malaria detection using microscopic images, 2019.
  • [24] J. A. Quinn, A. Andama, I. Munabi, F. N. Kiwanuka, “Automated blood smear analysis for mobile malaria diagnosis”, Mobile Pointof-Care Monitors and Diagnostic Device Design, 31-115, 2014.
  • [25] WebMicroscope “Institute for molecular medicine Finland and FIMM”. http://fimm.webmicroscope.net/Research/Momic/mamic., [Erişim Tarihi: 1 June 2018] F Abdurahman, KF Anlay and M Aliy, "Malaria Parasite Detection in Thick Blood Smear Microscopic Images Using Modified YOLOV3 and YOLOV4 Models", Research Square, 2020.
Year 2022, Volume: 13 Issue: 2, 271 - 277, 28.06.2022
https://doi.org/10.24012/dumf.1120289

Abstract

References

  • [1] A. Vijayalakshmi and B. Rajesh Kanna, "Deep learning approach to detect malaria from microscopic images", Multimedia Tools and Applications, vol. 79, 2019.
  • [2] S. Rajaraman et al., "Understanding the learned behavior of customized convolutional neural networks toward malaria parasite detection in thin blood smear images", J. Med. Imag., vol. 5, no. 3, Jul. 2018.
  • [3] Y. Dong et al., "Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells", Proc. IEEE EMBS Int. Conf. Biomed. Health Inform. (BHI), pp. 101-104, 2017.
  • [4] D. Bibin, M. S. Nair and P. Punitha, "Malaria Parasite Detection From Peripheral Blood Smear Images Using Deep Belief Networks," in IEEE Access, vol. 5, pp. 9099-9108, 2017, doi: 10.1109/ACCESS.2017.2705642.
  • [5] Z. Liang et al., "CNN-based image analysis for malaria diagnosis," 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2016, pp. 493-496, doi: 10.1109/BIBM.2016.7822567.
  • [6] J. Hung and A. Carpenter, "Applying Faster R-CNN for Object Detection on Malaria Images," 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017, pp. 808-813, doi: 10.1109/CVPRW.2017.112.
  • [7] B. N. Narayanan, R. Ali and R. C. Hardie, "Performance analysis of machine learning and deep learning architectures for malaria detection on cell images", Applications of Machine Learning, vol. 11139, pp. 111390W, September 2019.
  • [8] S. Muneer, S. Jatoi, B. Naz, “Detection of Noisy Blood Images Indicating Prodromal Diseases”, International Journal of Latest Trends in Engineering and Technology, Vol(12), Issue(6), pp. 001-006, 2018.
  • [9] lhncbc.nlm.nih.gov, “Lister Hill National Center for Biomedical Communications”, 2021. [Çevrimiçi]. https://ceb.nlm.nih.gov/repositories/malaria-datasets/ [Erişim Tarihi: 15.05.2021]
  • [10] J. Devlin, M. W. Chang, K. Lee, and K. Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In NAACL, 2019.
  • [11] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, …, et al., “An image is worth 16x16 words: Transformers for image recognition at scale”, In ICLR, 2021.
  • [12] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., "Attention is all you need", Advances in Neural Information Processing Systems, pp. 6000-6010, 2017.
  • [13] Q. Wang et al., "Learning deep transformer models for machine translation", Proc. Annu. Meeting Assoc. Comput. Linguistics, pp. 1810-1822, 2019.
  • [14] T. Czempiel, M. Paschali, D. Ostler, S. T. Kim, B. Busam and N. Navab, "OperA: Attention-regularized transformers for surgical phase recognition", arXiv:2103.03873, 2021, [online] Available: http://arxiv.org/abs/2103.03873.
  • [15] A. Diker, "Sıtma Hastalığının Sınıflandırılmasında Evrişimsel Sinir Ağlarının Performanslarının Karşılaştırılması", BEÜ Fen Bilim. Derg., vol. 9, no. 4, pp. 1825-1835, 2020.
  • [16] M. Masud, H. Alhumyani, S. S. Alshamrani, O. Cheikhrouhou, S. Ibrahim, G. Muhammad, et al., "Leveraging deep learning techniques for malaria parasite detection using mobile application", Wireless Commun. Mobile Comput., vol. 2020, pp. 1-15, Jul. 2020.
  • [17] T. Mahmud, & S. A. Fattah, “Automatic Diagnosis of Malaria from Thin Blood Smear Images using Deep Convolutional Neural Network with Multi-Resolution Feature Fusion”, arXiv preprint arXiv:2012.05350. 2020.
  • [18] B. N. Akılotu, Z. Kadiroğlu, A. Şengür and M. Kayaoğlu, “Evrişimsel Sinir Ağları ve Transfer Öğrenme Yöntemi Kullanılarak Sıtma Tespiti”, International Engineering and Science Symposium, Siirt, 2019.
  • [19] Whole slide image for malaria infected red blood cells, [Çevrimiçi] Erişim: http://peir-vm.path.uab.edu/debug.php?slide=IPLab11Malaria [Erişim Tarihi: 15.05.2021].
  • [20] F. Montalbo and A. Alon, "Empirical Analysis of a Fine-Tuned Deep Convolutional Model in Classifying and Detecting Malaria Parasites from Blood Smears", KSII Transactions on Internet and Information Systems, vol. 15, no. 1, 2021.
  • [21] E. Irmak, “A novel implementation of deep-learning approach on malaria parasite detection from thin blood cell images”, Electrica 21(2):216–224. 2021. https://doi.org/10.5152/electrica.2020.21004.
  • [22] T. Fatima and M.S Farid, "Automatic detection of Plasmodium parasites from microscopic blood images", Journal of Parasitic Diseases, vol. 44, no. 1, pp. 69-78, 2020.
  • [23] G. Saiprasath, N. Babu, J. ArunPriyan, R. Vinayakumar, V. Sowmya and K. Soman, Performance comparison of machine learning algorithms for malaria detection using microscopic images, 2019.
  • [24] J. A. Quinn, A. Andama, I. Munabi, F. N. Kiwanuka, “Automated blood smear analysis for mobile malaria diagnosis”, Mobile Pointof-Care Monitors and Diagnostic Device Design, 31-115, 2014.
  • [25] WebMicroscope “Institute for molecular medicine Finland and FIMM”. http://fimm.webmicroscope.net/Research/Momic/mamic., [Erişim Tarihi: 1 June 2018] F Abdurahman, KF Anlay and M Aliy, "Malaria Parasite Detection in Thick Blood Smear Microscopic Images Using Modified YOLOV3 and YOLOV4 Models", Research Square, 2020.
There are 25 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

İbrahim Tuncel 0000-0002-4965-5134

Abdülkadir Albayrak 0000-0002-0738-871X

Mehmet Akın 0000-0002-4965-5134

Early Pub Date June 28, 2022
Publication Date June 28, 2022
Submission Date May 26, 2022
Published in Issue Year 2022 Volume: 13 Issue: 2

Cite

IEEE İ. Tuncel, A. Albayrak, and M. Akın, “Öz Dikkat Mekanizması Tabanlı Görü Dönüştürücü Kullanılarak Sıtma Parazit Tespiti”, DUJE, vol. 13, no. 2, pp. 271–277, 2022, doi: 10.24012/dumf.1120289.
DUJE tarafından yayınlanan tüm makaleler, Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır. Bu, orijinal eser ve kaynağın uygun şekilde belirtilmesi koşuluyla, herkesin eseri kopyalamasına, yeniden dağıtmasına, yeniden düzenlemesine, iletmesine ve uyarlamasına izin verir. 24456