Sıtma Hastalığının Otomatik Tespiti için EfficientNet Tabanlı Segmentasyon Modellerinin Performans Analizi

Yıl 2023, Cilt: 16 Sayı: 3, 167 - 176, 31.07.2023

Murat Uçar , Emine Uçar

https://doi.org/10.17671/gazibtd.1264480

Öz

Sıtma, tropik bölgelerde yaygın olan Plasmodium parazitinin neden olduğu bir hastalıktır. Dünyanın en ölümcül hastalıklarından biri olan sıtmanın teşhisinde yaygın olarak kullanılan geleneksel yöntemler, şüpheli kişilerden alınan kan örneklerinin manuel olarak incelendiği mikroskobik teşhis yöntemleri veya insan hatalarına duyarlı hızlı teşhis testleridir. Bu işlemler ucuzdur, ancak deneyimli ve nitelikli klinisyenlere ihtiyaç vardır. Bu eksiklik nedeniyle, modern teşhis araçları hastalıkla mücadelede çok önemlidir. Bu çalışmada tıbbi görüntülerden hastalık teşhisinde faydalı çözümler sunan derin öğrenme yöntemlerine dayalı bir yaklaşım kullanılmıştır. Önerilen yaklaşımda, U-Net, Pyramid Scene Parsing Network, LinkNet ve Feature Pyramid Network segmentasyon yöntemleri, EfficientNet derin öğrenme modelinin 8 farklı önceden eğitilmiş varyantı ile modifiye edilerek gelişmiş modeller elde edilmiştir. Bu modeller ile yapılan sıtma segmentasyonunda %91,50 ile en yüksek Dice skoru EfficientNetB6 ile U-Net modelinin kullanımında elde edilmiştir. Bu model, geleneksel yöntemlere kıyasla parazitleri tespit etmek için daha hızlı ve daha sağlam bir çözüm sunar

Anahtar Kelimeler

Sıtma tespiti, derin öğrenme, segmentasyon, EfficientNet

Teşekkür

Sayın editör, Değerli çabalarınız ve ayrıca makaleyle ilgili değerli yorumlarınız için şimdiden çok teşekkür ederim. Saygılarımla, Dr. Murat UÇAR

Performance Analysis of EfficientNet Based Segmentation Models for Automatic Detection of Malaria Disease

Öz

Malaria is a disease caused by the Plasmodium parasite, which is common in the tropics. The traditional methods commonly used to diagnose malaria, one of the world's deadliest diseases, are microscopic diagnostic methods in which blood samples taken from suspected individuals are manually examined, or rapid diagnostic tests that are sensitive to human errors. These processes are inexpensive, but experienced and qualified clinicians are needed. Due to this shortcoming, modern diagnostic tools are crucial in the struggle against the disease. In this study, an approach based on deep learning methods was used, which offers beneficial solutions in the diagnosis of disease from medical images. In the proposed approach, U-Net, Pyramid Scene Parsing Network, LinkNet, and Feature Pyramid Network segmentation methods were modified with 8 different pre-trained variants of the EfficientNet deep learning model to obtain improved models. In the malaria segmentation performed with these models, the highest Dice score of 91.50% was achieved in the use of the U-Net model with EfficientNetB6. This model offers a faster and more robust solution to detecting parasites compared to traditional methods.

Anahtar Kelimeler

Malaria detection, deep learning, segmentation, EfficientNet

Kaynakça

• Internet: WHO, World Malaria Report 2021, https://www.who.int/publications/i/item/9789240040496, 29.07.2022.
• K. S. Makhija, S. Maloney, and R. Norton, “The utility of serial blood film testing for the diagnosis of malaria”, Pathology, 47(1), 68–70, 2015.
• B. Nadjm and R. H. Behrens, “Malaria: An update for physicians”, Infectious Disease Clinics, 26(2), 243–259, 2012.
• L. Zekar and T. Sharman, Plasmodium Falciparum Malaria, StatPearls Publishing, Treasure Island (FL), 2022.
• N. M. Pham, W. Karlen, H.-P. Beck, and E. Delamarche, “Malaria and the ‘last’parasite: how can technology help?”, Malaria Journal, 17(1), 1–16, 2018.
• A. Mbanefo and N. Kumar, “Evaluation of malaria diagnostic methods as a key for successful control and elimination programs”, Tropical Medicine and Infectious Disease, 5(2), 102, 2020.
• M. L. Wilson, “Laboratory diagnosis of malaria: conventional and rapid diagnostic methods”, Archives of Pathology and Laboratory Medicine, 137(6), 805–811, 2013.
• S. Shambhu, D. Koundal, P. Das, V. T. Hoang, K. Tran-Trung, and H. Turabieh, “Computational Methods for Automated Analysis of Malaria Parasite Using Blood Smear Images: Recent Advances”, Computational Intelligence and Neuroscience, 2022.
• Z. Liang et al., “CNN-based image analysis for malaria diagnosis”, 2016 IEEE international conference on bioinformatics and biomedicine (BIBM), 493–496, Shenzhen, China, 15-18 December, 2016.
• S. Rajaraman et al., “Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images”, PeerJ, 6, e4568, 2018.
• D. Bibin, M. S. Nair, and P. Punitha, “Malaria parasite detection from peripheral blood smear images using deep belief networks”, IEEE Access, 5, 9099–9108, 2017.
• K. Sriporn, C.-F. Tsai, C.-E. Tsai, and P. Wang, “Analyzing malaria disease using effective deep learning approach,” Diagnostics, 10(10), 744, 2020.
• M. Umer, S. Sadiq, M. Ahmad, S. Ullah, G. S. Choi, and A. Mehmood, “A novel stacked CNN for malarial parasite detection in thin blood smear images”, IEEE Access, 8, 93782–93792, 2020.
• [14] A. Abubakar, M. Ajuji, and I. U. Yahya, “DeepFMD: Computational Analysis for Malaria Detection in Blood-Smear Images Using Deep-Learning Features”, Applied System Innovation, 4(4), 82, 2021.
• [15] A. Rahman, H. Zunair, T. R. Reme, M. S. Rahman, and M. R. C. Mahdy, “A comparative analysis of deep learning architectures on high variation malaria parasite classification dataset”, Tissue and Cell, 69, 101473, 2021.
• M. R. Islam et al., “Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images”, Sensors, 22(12), 4358, 2022.
• S. S. Abbas and T. M. H. Dijkstra, “Detection and stage classification of Plasmodium falciparum from images of Giemsa stained thin blood films using random forest classifiers”, Diagnostic pathology, 15(1), 1–11, 2020.
• A. S. A. Nasir, M. Y. Mashor, and Z. Mohamed, “Segmentation based approach for detection of malaria parasites using moving k-means clustering”, 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, 653–658, Malaysia, 17-19 December, 2012.
• V. V Panchbhai, L. B. Damahe, A. V Nagpure, and P. N. Chopkar, “RBCs and parasites segmentation from thin smear blood cell images”, International Journal of Image, Graphics and Signal Processing, 4(10), 54, 2012.
• J. Hung and A. Carpenter, “Applying faster R-CNN for object detection on malaria images,” Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 56–61, Honolulu, USA, 21-26 July, 2017.
• M. S. Davidson et al., “Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks”, Biological imaging, 1, e2, 2021.
• D. R. Loh, W. X. Yong, J. Yapeter, K. Subburaj, and R. Chandramohanadas, “A deep learning approach to the screening of malaria infection: Automated and rapid cell counting, object detection and instance segmentation using Mask R-CNN”, Computerized Medical Imaging and Graphics, 88, 101845, 2021.
• Z. Yang, H. Benhabiles, K. Hammoudi, F. Windal, R. He, and D. Collard, “A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images”, Neural Computing and Applications, 34(17), 14223–14238, 2022.
• Internet: Kaggle Dataset, Malaria Segmentation, https://www.kaggle.com/datasets/niccha/malaria-segmentation 01.06.2022.
• O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation”, International Conference on Medical image computing and computer-assisted intervention, 234–24, Munich, Germany, 5-9 October, 2015.
• H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network”, Proceedings of the IEEE conference on computer vision and pattern recognition, 2881–2890, Honolulu, USA, 21-26 July, 2017.
• M. Long, Y. Cao, J. Wang, and M. Jordan, “Learning transferable features with deep adaptation networks”, International conference on machine learning, 97–105, Lille, France, 6-11 July, 2015.
• A. Chaurasia and E. Culurciello, “Linknet: Exploiting encoder representations for efficient semantic segmentation,” 2017 IEEE Visual Communications and Image Processing (VCIP), 1–4, St. Petersburg, USA, 10-13 December, 2017.
• T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” Proceedings of the IEEE conference on computer vision and pattern recognition, 2117–2125, Honolulu, USA, 21-26 July, 2017.
• M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” 36th International Conference on Machine Learning (ICML), 10691–10700, Long Beach, California, 9-15 June, 2019.
• T.Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” Proceedings of the IEEE international conference on computer vision, 2980–2988, Venice, Italy, 22-29 October, 2017.
• F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” 2016 fourth international conference on 3D vision (3DV), 565–571, Stanford University, California, USA, 25 - 28 October, 2016.
• Internet: P. Yakubovskiy, Segmentation Models, GitHub repository. https://github.com/qubvel/segmentation_models, 01.07.2022
• J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?,” arXiv Prepr. arXiv1411.1792, 2014.
• H. Li, P. Xiong, J. An, and L. Wang, “Pyramid attention network for semantic segmentation,” arXiv Prepr. arXiv1805.10180, 2018.