Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2020, , 1825 - 1835, 25.12.2020
https://doi.org/10.17798/bitlisfen.783031

Öz

Kaynakça

  • 1- [1] Liang, Z. 2017. Automatic Image Recognition of Rapid Malaria Emergency Diagnosis: A Deep Neural Network Approach.
  • 2- [2] Sunarko, B., Bottema, M., Iksan, N., Hudaya, K. A., & Hanif, M. S. 2020. Red blood cell classification on thin blood smear images for malaria diagnosis. In Journal of Physics: Conference Series (Vol. 1444, No. 1, p. 012036). IOP Publishing.
  • 3-[3] Saritha, J., Spandana, P., Raju, A. M., & Goud, J. E. A., 2019. Classifying Malaria Cell Images Dataset using Machine Learning Algorithms. International Journal for Research in Applied Science & Engineering Technology, 7(4):2409–2412.
  • 4-[4] Suriya, M., Chandran, V., & Sumithra, M. G. 2019. Enhanced deep convolutional neural network for malarial parasite classification. International Journal of Computers and Applications, 1-10.
  • 5-[5] Bairagi, V. K., & Charpe, K. C. 2016. Comparison of texture features used for classification of life stages of malaria parasite. International journal of biomedical imaging, 2016.
  • 6-[6] Malihi, L., Asl, K. A., & Behbahani, A. 2015. Improvement in Classification Accuracy Rate Using Multiple Classifier Fusion Towards Computer Vision Detection of Malaria Parasite (Plasmodium vivax). Jundishapur Journal of Health Sciences, 7(3).
  • 7-[7] Das, S., Sony, P., & Jyothi, RL., 2018. Automated Identification & Classification of Malarial Parasites in Thin Blood Smear Images. International Research Journal of Engineering and Technology (IRJET) :4214–4218.
  • 8-[8] Saiprasath, G. B., Babu, N., ArunPriyan, J., Vinayakumar, R., Sowmya, V., & Soman, K. P. 2019. Performance comparison of machine learning algorithms for malaria detection using microscopic images.
  • 9-[9] Razzak, M. I. 2015. Malarial parasite classification using recurrent neural network. Int J Image Process, 9, 69.
  • 10-[10] Diker, A., & Avcı, E. 2019. Feature Extraction of ECG Signal by using Deep Feature. In 2019 7th International Symposium on Digital Forensics and Security (ISDFS) (pp. 1-6). IEEE.
  • 11-[11] Özyurt, F. 2019. Efficient deep feature selection for remote sensing image recognition with fused deep learning architectures. The Journal of Supercomputing, 1-19.
  • 12-[12] Zhang, X., Zhou, X., Lin, M., & Sun, J. 2018. Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6848-6856).
  • 13-[13] Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360.
  • 14-[14] Narayanan, B. N., Ali, R., & Hardie, R. C. 2019. Performance analysis of machine learning and deep learning architectures for malaria detection on cell images. In Applications of Machine Learning (Vol. 11139, p. 111390W). International Society for Optics and Photonics.
  • 15-[15] Cömert, Z. 2019. Otitis media için evrişimsel sinir ağlarına dayalı entegre bir tanı sistemi. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 8(4), 1498-1511.
  • 16-[16] Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
  • 17-[17] Guan, C. Z. 2019. Realtime Multi-Person 2D Pose Estimation using ShuffleNet. In 2019 14th International Conference on Computer Science & Education (ICCSE) (pp. 17-21). IEEE.
  • 18-[18] Diker, A., Cömert, Z., Avcı, E., & Velappan, S. 2018. Intelligent system based on Genetic Algorithm and support vector machine for detection of myocardial infarction from ECG signals. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  • 19-[19] Diker, A., Cömert, Z., Avcı, E., Toğaçar, M., & Ergen, B. 2019. A Novel Application based on Spectrogram and Convolutional Neural Network for ECG Classification. In 2019 1st International Informatics and Software Engineering Conference (UBMYK) (pp. 1-6). IEEE.
  • 20-[20] Dıker, A., Avci, E., Cömert, Z., Avci, D., Kaçar, E., & Serhatlioğlu, İ. 2018. Classification of ECG signal by using machine learning methods. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  • 21-[21] Diker, A., Cömert, Z., & Avcı, E. 2017. A diagnostic model for identification of myocardial infarction from electrocardiography signals. Bitlis Eren University Journal of Science and Technology, 7(2), 132-139.
  • 22-[22] Muneer, S., Jatoi, S., Naz, B. 2018. Detection of Noisy Blood Images Indicating Prodromal Diseases. pp:1–6.
  • 23-[23] Vijayalakshmi, A. 2019. Deep learning approach to detect malaria from microscopic images. Multimedia Tools and Applications, 1-21.
  • 24-[24] Pan, W. D., Dong, Y., & Wu, D. 2018. Classification of malaria-infected cells using deep convolutional neural networks. Machine Learning: Advanced Techniques and Emerging Applications, 159.

Sıtma Hastalığının Sınıflandırılmasında Evrişimsel Sinir Ağlarının Performanslarının Karşılaştırılması

Yıl 2020, , 1825 - 1835, 25.12.2020
https://doi.org/10.17798/bitlisfen.783031

Öz

Sıtma, Plasmodium adlı bir kan parazitinin neden olduğu ciddi bir hastalıktır. Sıtmayı teşhis etmenin standart yolu, parazit bulaşmış kırmızı kan hücreleri için kan bulaşmalarını mikroskop altında uzmanlar tarafından görsel olarak incelenmesidir. Bu yöntem verimsizdir ve tanı, muayeneyi yapan kişinin deneyimine ve bilgisine bağlıdır. Daha önce teşhis için sıtma kan hücrelerine makine öğrenimine dayalı otomatik görüntü tanıma teknolojileri uygulanmıştır. Bu çalışmanın amacı, sıtma hücrelerinin parazit veya enfekte olmayan olarak ayırmak için, önceden eğitilmiş evrişimsel sinir ağlarına dayalı bütünleşik bir tanı sistemi önerilmiştir. Bu çalışmada sıtma hücrelerini sınıflandırmak için Ulusal Sağlık Enstitüleri'nde (NIH) toplam 27558 imge kullanılmıştır. Deneysel çalışmalar sonucunda, sıtma hücrelerinin sınıflandırılmasında, sırasıyla AlexNet, GoogleNet, SqueezeNet, ShuffleNet ESA modelleri ile % 95.77, % 96.31, % 95.95, % 96.44 ve 0.9880, 0.9887, 0.9888, 0.9923 EAA performans değerleri elde edilmiştir. Sıtma hücrelerinin sınıflandırılmasında en iyi sonuca, %96.44 Doğruluk değeri ile ShuffleNet CNN modeli kullanılarak ulaşılmıştır.

Kaynakça

  • 1- [1] Liang, Z. 2017. Automatic Image Recognition of Rapid Malaria Emergency Diagnosis: A Deep Neural Network Approach.
  • 2- [2] Sunarko, B., Bottema, M., Iksan, N., Hudaya, K. A., & Hanif, M. S. 2020. Red blood cell classification on thin blood smear images for malaria diagnosis. In Journal of Physics: Conference Series (Vol. 1444, No. 1, p. 012036). IOP Publishing.
  • 3-[3] Saritha, J., Spandana, P., Raju, A. M., & Goud, J. E. A., 2019. Classifying Malaria Cell Images Dataset using Machine Learning Algorithms. International Journal for Research in Applied Science & Engineering Technology, 7(4):2409–2412.
  • 4-[4] Suriya, M., Chandran, V., & Sumithra, M. G. 2019. Enhanced deep convolutional neural network for malarial parasite classification. International Journal of Computers and Applications, 1-10.
  • 5-[5] Bairagi, V. K., & Charpe, K. C. 2016. Comparison of texture features used for classification of life stages of malaria parasite. International journal of biomedical imaging, 2016.
  • 6-[6] Malihi, L., Asl, K. A., & Behbahani, A. 2015. Improvement in Classification Accuracy Rate Using Multiple Classifier Fusion Towards Computer Vision Detection of Malaria Parasite (Plasmodium vivax). Jundishapur Journal of Health Sciences, 7(3).
  • 7-[7] Das, S., Sony, P., & Jyothi, RL., 2018. Automated Identification & Classification of Malarial Parasites in Thin Blood Smear Images. International Research Journal of Engineering and Technology (IRJET) :4214–4218.
  • 8-[8] Saiprasath, G. B., Babu, N., ArunPriyan, J., Vinayakumar, R., Sowmya, V., & Soman, K. P. 2019. Performance comparison of machine learning algorithms for malaria detection using microscopic images.
  • 9-[9] Razzak, M. I. 2015. Malarial parasite classification using recurrent neural network. Int J Image Process, 9, 69.
  • 10-[10] Diker, A., & Avcı, E. 2019. Feature Extraction of ECG Signal by using Deep Feature. In 2019 7th International Symposium on Digital Forensics and Security (ISDFS) (pp. 1-6). IEEE.
  • 11-[11] Özyurt, F. 2019. Efficient deep feature selection for remote sensing image recognition with fused deep learning architectures. The Journal of Supercomputing, 1-19.
  • 12-[12] Zhang, X., Zhou, X., Lin, M., & Sun, J. 2018. Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6848-6856).
  • 13-[13] Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360.
  • 14-[14] Narayanan, B. N., Ali, R., & Hardie, R. C. 2019. Performance analysis of machine learning and deep learning architectures for malaria detection on cell images. In Applications of Machine Learning (Vol. 11139, p. 111390W). International Society for Optics and Photonics.
  • 15-[15] Cömert, Z. 2019. Otitis media için evrişimsel sinir ağlarına dayalı entegre bir tanı sistemi. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 8(4), 1498-1511.
  • 16-[16] Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
  • 17-[17] Guan, C. Z. 2019. Realtime Multi-Person 2D Pose Estimation using ShuffleNet. In 2019 14th International Conference on Computer Science & Education (ICCSE) (pp. 17-21). IEEE.
  • 18-[18] Diker, A., Cömert, Z., Avcı, E., & Velappan, S. 2018. Intelligent system based on Genetic Algorithm and support vector machine for detection of myocardial infarction from ECG signals. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  • 19-[19] Diker, A., Cömert, Z., Avcı, E., Toğaçar, M., & Ergen, B. 2019. A Novel Application based on Spectrogram and Convolutional Neural Network for ECG Classification. In 2019 1st International Informatics and Software Engineering Conference (UBMYK) (pp. 1-6). IEEE.
  • 20-[20] Dıker, A., Avci, E., Cömert, Z., Avci, D., Kaçar, E., & Serhatlioğlu, İ. 2018. Classification of ECG signal by using machine learning methods. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  • 21-[21] Diker, A., Cömert, Z., & Avcı, E. 2017. A diagnostic model for identification of myocardial infarction from electrocardiography signals. Bitlis Eren University Journal of Science and Technology, 7(2), 132-139.
  • 22-[22] Muneer, S., Jatoi, S., Naz, B. 2018. Detection of Noisy Blood Images Indicating Prodromal Diseases. pp:1–6.
  • 23-[23] Vijayalakshmi, A. 2019. Deep learning approach to detect malaria from microscopic images. Multimedia Tools and Applications, 1-21.
  • 24-[24] Pan, W. D., Dong, Y., & Wu, D. 2018. Classification of malaria-infected cells using deep convolutional neural networks. Machine Learning: Advanced Techniques and Emerging Applications, 159.
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Aykut Diker 0000-0002-1207-8548

Yayımlanma Tarihi 25 Aralık 2020
Gönderilme Tarihi 20 Ağustos 2020
Kabul Tarihi 19 Ekim 2020
Yayımlandığı Sayı Yıl 2020

Kaynak Göster

IEEE A. Diker, “Sıtma Hastalığının Sınıflandırılmasında Evrişimsel Sinir Ağlarının Performanslarının Karşılaştırılması”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, c. 9, sy. 4, ss. 1825–1835, 2020, doi: 10.17798/bitlisfen.783031.



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