İki Sıtma Veri Kümesinde Sıtma Hastalığı Tespitine Yönelik Yeni Bir Derin Öğrenme Yaklaşımı

Year 2023, Volume: 10 Issue: 2, 254 - 272, 30.11.2023

İbrahim Çetiner , Halit Çetiner

https://doi.org/10.35193/bseufbd.1064187

Abstract

Sıtma, dişi sivrisineklerin ısırmasıyla insanlara bulaşan bulaşıcı bir ateşli hastalıktır. Bu hastalığın kısa sürede teşhis edilmesi önemlidir. Belirli bir probleme matematiksel olarak en iyi sayısal çözümü bulmak çoğu bölüm için en önemli konudur. Bu mantıkla geliştirilen derin öğrenme tabanlı sistemde, gerçek veriler ile modelin tahmin edilen sonucu arasındaki fark, kayıp fonksiyonları kullanılarak ölçülmektedir. Derin öğrenme modelinin eğitim sürecinde tahminlerdeki hata oranını en aza indirmek için modelde kullanılan ağırlık değerlerinin güncellenmesi gerekmektedir. Yapılan güncelleme işlemi, modelin tahmin sonucu üzerinde önemli bir etkiye sahiptir. Bu makale, sıtmayı kısa sürede teşhis etmeye yardımcı olacak yeni bir derin öğrenme tabanlı sıtma sınıflandırma yöntemi sunmaktadır. Bu amaçla, 21 katmanlı yeni bir Konvolüsyonel Sinir Ağı (CNN) modeli önerilmiştir. Önerilen bu model enfekte ve enfekte olmayan ince kırmızı kan hücresi görüntülerini sınıflandırmak için tasarlanmıştır. İnce kırmızı kan hücresi numunesi görüntüleri kullanılarak, Nadam ve RMSprop optimizasyon teknikleri ile %95 doğruluk elde edilmiştir. Elde edilen sonuçlar, önerilen yöntemin her bir optimizasyon algoritmasına göre etkinliğini göstermektedir.

Keywords

Sıtma, CNN, Optimizasyon Algoritmaları, Sınıflandırma

A Novel Deep Learning Approach to Malaria Disease Detection on Two Malaria Datasets

Abstract

Malaria is a contagious febrile disease transmitted to humans by the bite of female mosquitoes. It is important to diagnose this disease in a short period of time. Finding the mathematically best numerical solution to a particular problem is the most important issue for most departments. In deep learning-based systems developed, the difference between the real data and the predicted result of the model is measured using loss functions. To minimize the error rate in the predictions during the training process of deep learning models, the weight values used in the model should be updated. This update process has a significant effect on the model prediction result. This article presents a new deep learning-based malaria detection method that will help diagnose malaria in a short time. A new 21-layer Convolutional Neural Network (CNN) model is designed and proposed to describe infected and uninfected thin red blood cell images. By using thin red blood cell sample images, 95% accuracy was achieved with Nadam and RMSprop optimization techniques. The results obtained show the efficiency of the proposed method according to each optimization algorithm.

Keywords

Malaria, CNN, optimization algorithms, classification

References

• Ikerionwu, C., Ugwuishiwu, C., Okpala, I., James, I., Okoronkwo, M., Nnadi, C., & Ike, A. (2022). Application of Machine and Deep Learning Algorithms in Optical Microscopic Detection of Plasmodium Parasites: A Malaria Diagnostic Tool for the Future. Photodiagnosis and Photodynamic Therapy, 103198.
• Bonilla, J. A. (2006). Assessing the function of the aspartic proteinases of the Plasmodium falciparum digestive vacuole using gene-knockout strategies. University of Florida.
• Tangpukdee, N., Duangdee, C., Wilairatana, P., & Krudsood, S. (2009). Malaria Diagnosis: A Brief Review. The Korean Journal of Parasitology, 47(2), 93.
• World Health Organization. (2021). World malaria report 2021. World Health Organization 2021.
• Das, D. K., Mukherjee, R., & Chakraborty, C. (2015). Computational microscopic imaging for malaria parasite detection: a systematic review. Journal of Microscopy, 260(1), 1–19.
• Mitiku, K., Mengistu, G., & Gelaw, B. (2003). The reliability of blood film examination for malaria at the peripheral health unit. Ethiopian Journal of Health Development, 17(3), 197–204.
• Chavan, S. N., & Sutkar, A. M. (2014). Malaria disease identification and analysis using image processing. Int. J. Latest Trends Eng. Technol, 3(3), 218–223.
• Siłka, W., Wieczorek, M., Siłka, J., & Woźniak, M. (2023). Malaria Detection Using Advanced Deep Learning Architecture. Sensors, 23(3), 1501.
• Vijayalakshmi A, & Rajesh Kanna B. (2020). Deep learning approach to detect malaria from microscopic images. Multimedia Tools and Applications, 79(21–22), 15297–15317.
• Poostchi, M., Silamut, K., Maude, R. J., Jaeger, S., & Thoma, G. (2018). Image analysis and machine learning for detecting malaria. Translational Research, 194, 36–55.
• Rajaraman, S., Antani, S. K., Poostchi, M., Silamut, K., Hossain, M. A., Maude, R. J., & Thoma, G. R. (2018). Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images. PeerJ, 6, e4568.
• Díaz, G., González, F. A., & Romero, E. (2009). A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images. Journal of Biomedical Informatics, 42(2), 296–307.
• Shuleenda Devi, S., Sheikh, S. A., Talukdar, A., & Laskar, R. H. (2016). Malaria Infected Erythrocyte Classification Based on the Histogram Features using Microscopic Images of Thin Blood Smear. Indian Journal of Science and Technology, 9(45).
• Das, D. K., Maiti, A. K., & Chakraborty, C. (2015). Automated system for characterization and classification of malaria‐infected stages using light microscopic images of thin blood smears. Journal of microscopy, 257(3), 238–252.
• Dong, Y., Jiang, Z., Shen, H., David Pan, W., Williams, L. A., Reddy, V. V. B., & Bryan, A. W. (2017). Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells. In 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 101–104, IEEE.
• Van-Quoc, V., & Thai-Nghe, N. (2023). Skin Diseases Detection with Transfer Learning. In Proceedings of International Conference on Data Science and Applications: ICDSA 2022, 1, 139–150, Springer.
• Hamedani-KarAzmoudehFar, F., Tavakkoli-Moghaddam, R., Tajally, A. R., & Aria, S. S. (2023). Breast cancer classification by a new approach to assessing deep neural network-based uncertainty quantification methods. Biomedical Signal Processing and Control, 79, 104057.
• Gupta, K., & Bajaj, V. (2023). Deep learning models-based CT-scan image classification for automated screening of COVID-19. Biomedical Signal Processing and Control, 80, 104268.
• Odusami, M., Maskeliunas, R., Damaševičius, R., & Misra, S. (2021). Comparable study of pre-trained model on alzheimer disease classification. In Computational Science and Its Applications–ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part V 21, 63–74. Springer.
• Rajaraman, S., Jaeger, S., & Antani, S. K. (2019). Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images. PeerJ, 7, e6977.
• Kassim, Y. M., Yang, F., Yu, H., Maude, R. J., & Jaeger, S. (2021). Diagnosing malaria patients with plasmodium falciparum and vivax using deep learning for thick smear images. Diagnostics, 11(11), 1994.
• Yu, H., Yang, F., Rajaraman, S., Ersoy, I., Moallem, G., Poostchi, M., & Jaeger, S. (2020). Malaria Screener: a smartphone application for automated malaria screening. BMC Infectious Diseases, 20(1), 1–8.
• Uddin, S. (2020). Malaria Parasite Image. Retrieved from https://www.kaggle.com/datasets/saife245/malaria-parasite-image-malaria-species.
• Goutte, C., & Gaussier, E. (2005). A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation. In Lecture Notes in Computer Science, 3408, 345–359.
• Aggarwal, C. C. (2018). Neural networks and deep learning. Springer, 10, 973–978.
• Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2021). Dive into Deep Learning. Journal of the American College of Radiology, 17(11), 437–516.
• McMahan, H. B., Holt, G., Sculley, D., Young, M., Ebner, D., Grady, J., & Kubica, J. (2013). Ad click prediction. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (Vol. 9, pp. 1222–1230). New York, NY, USA: ACM.
• Finsveen, L. (2018). Time-series predictions with Recurrent Neural Networks, (June), 21–22.
• Hoang, N.-D. (2021). Automatic Impervious Surface Area Detection Using Image Texture Analysis and Neural Computing Models with Advanced Optimizers. Computational Intelligence and Neuroscience, 2021, 1–17.
• Kingma, D., & Ba, J. (2014). Adam: A Method for Stochastic Optimization. International Conference on Learning Representations.
• Çetiner, İ. (2022). Konvolüsyonel Sinir Ağı Kullanılarak Sıtma Hastalığı Sınıflandırılması. Journal, 9(17), 273–286.
• Raihan, M. J., & Nahid, A.-A. (2022). Malaria cell image classification by explainable artificial intelligence. Health and Technology, 12(1), 47–58.
• Khan, A., Gupta, K. D., Venugopal, D., & Kumar, N. (2020). CIDMP: Completely Interpretable Detection of Malaria Parasite in Red Blood Cells using Lower-dimensional Feature Space. In 2020 International Joint Conference on Neural Networks (IJCNN), 1–8.
• Montalbo, F. J. P., & Alon, A. S. (2021). 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, 15(1), 147–165.
• Reddy, A. S. B., & Juliet, D. S. (2019). Transfer Learning with ResNet-50 for Malaria Cell-Image Classification. In 2019 International Conference on Communication and Signal Processing (ICCSP), 945–949.
• Keskar, N. S., & Socher, R. (2017). Improving generalization performance by switching from adam to sgd. arXiv preprint arXiv:1712.07628.
• Oyewola, D. O., Dada, E. G., Misra, S., & Damaševičius, R. (2022). A Novel Data Augmentation Convolutional Neural Network for Detecting Malaria Parasite in Blood Smear Images. Applied Artificial Intelligence, 36(1), 2033473.
• Das, D. K., Ghosh, M., Pal, M., Maiti, A. K., & Chakraborty, C. (2013). Machine learning approach for automated screening of malaria parasite using light microscopic images. Micron, 45, 97–106.
• Singla, N., & Srivastava, V. (2020). Deep learning enabled multi-wavelength spatial coherence microscope for the classification of malaria-infected stages with limited labelled data size. Optics & Laser Technology, 130(September 2019), 106335.