COVID-19 Teşhisi İçin Derin Topluluk Öğrenmeye Dayalı Modellerde Temel Füzyon Tekniklerinin Etkisi

Year 2023, , 1 - 17, 20.12.2023

Yaşar Daşdemir , Hafize Arduç

https://doi.org/10.47495/okufbed.1220413

Abstract

Küresel salgın hastalık (pandemi) olarak deklare edilen koronavirüs hastalığı (COVID-19), yeni bir viral solunum yolu hastalığıdır. Hastalık insandan insana damlacık veya temas yoluyla bulaşmaktadır. Hastalığın yayılmasını önlemek için hızlı tanı oranları ile hastalığı erken tespit etmek çok önemlidir. Ancak uzun süren patolojik laboratuvar testleri ve test sonuçlarındaki düşük tanı oranı araştırmacıları farklı teknikleri uygulamaya yöneltmiştir. Radyolojik görüntüleme ile birlikte derin öğrenme tekniklerinin uygulanması bu hastalığın doğru tespitinde oldukça önemli bir yere sahiptir. Bu çalışmada, COVID-19 X-ray veri seti kullanılarak temel füzyon fonksiyonlarının topluluk öğrenme algoritmaları üzerindeki sınıflandırma performansına etkisi araştırılmıştır. Farklı derin öğrenme modellerini birleştirmek için iki farklı topluluk modeli oluşturuldu; Topluluk-1 (Ens-1) ve Topluluk-2 (Ens-2). Bu topluluk modellerinde Maks, Mod, Toplam, Ortalama ve Çarpım gibi temel füzyon fonksiyonları test edilmiştir. Elde edilen değerler incelendiğinde Max ve Product temel füzyon fonksiyonlarının sınıflandırma performansı üzerinde olumlu bir etkiye sahip olduğu görülmektedir. Çoklu sınıflandırmada, hem Ens-1 hem de Ens-2 için Max işlevi sırasıyla %85 ve %86 doğruluk oranıyla öne çıkıyor. Product fonksiyonu, ikili sınıflandırmada %99 ile en yüksek performansı elde etmiştir. Sonuçlar, füzyon yöntemlerinin ikili sınıflandırmada daha iyi sınıflandırma performansı elde edebileceğini göstermektedir.

Keywords

COVID-19, Topluluk öğrenimi, Transfer öğrenimi, Füzyon, Maksimum oylama

The Effect of Basic Fusion Techniques in Deep Ensemble Learning-Based Models For Covid-19 Diagnosis

Abstract

The coronavirus disease (COVID-19), declared as a global epidemic disease (pandemic), is a new viral respiratory disease. The disease is transmitted from person to person through droplets or contact. İt is very important to detect the disease early with rapid diagnosis rates to prevent the spread of the disease. However, long-term pathological laboratory tests and low diagnosis rates in test results led researchers to apply different techniques. Radiological imaging has begun to be used to monitor COVID-19 disease as well as being useful in detecting various lung diseases. The application of deep learning techniques together with radiological imaging has a very important place in the correct detection of this disease. İn this study, the effect of basic fusion functions on classification performance on ensemble learning algorithms was investigated using the COVİD-19 X-ray dataset. Two different ensemble models were created to combine different deep learning models; Ensemble-1 (Ens-1) ve Ensemble-2 (Ens-2). The basic fusion rules of Max, Mode, Sum, Average, and Product were tested in these ensemble models. When the obtained values are examined, it is seen that the Max and Product basic fusion functions have a positive effect on the classification performance. İn multi-classification, the Max function for both Ens-1 and Ens-2 becomes prominent with an accuracy rate of 85% and 86%, respectively. The Product function achieved the highest performance with 99% in binary classification. The results show that the fusion methods can achieve better classification performance in binary classification.

Keywords

Ensemble learning, Transfer learning, Fusion, Max voting, Covid 19

References

• Abraham B., Nair MS. Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier. Biocybernetics and Biomedical Engineering 2020; 40(6): 1436-1445.
• Alimadadi A., Aryal S., Manandhar I., Munroe PB., Joe B., Cheng X. Artificial intelligence and machine learning to fight COVID-19. Physiol Genomics 2020; 52(4): 200–202.
• Ardakani AA., Kanafi AR., Acharya UR., Khadem N., Mohammadi A. Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Computers in Biology and Medicine 2020; 121(103795): 1-12.
• Bozkurt F. Derin öğrenme tekniklerini kullanarak akciğer x-ray görüntülerinden COVID-19 tespiti. European Journal of Science and Technology 2021; 24: 149-156.
• Cohen JP., Morrison P., Dao L. COVID-19 image data collection. Computers and Education 2020; 164(11597): 1-11.
• Dey N., Zhang YD., Rajinikanth V., Pugalenthi R., Raja NSM. Customized VGG19 architecture for pneumonia detection in chest x-rays. Pattern Recognition Letters 2021; 143(1): 67–74.
• Hassantabar S., Ahmadi M., Sharif A. Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches. Chaos, Solitons and Fractals 2020; 140(110170): 1-11.
• Huang G., Liu Z., van der Maaten L., Weinberger KQ. Densenet: densely connected convolutional networks. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2017; 30(1): 82–84.
• Ismael AM., Şengür A. Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Systems with Applications 2021; 164(114054): 1-11.
• Kırbas I., Sözen A., Tuncer AD., Kazancıoglu FS. Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approach. American Physiological Society Bethesda 2020; 138(110015): 1-11.
• Korkmaz A., Atila BÜ. Derin öğrenme teknikleriyle akciğer röntgeninden Covid-19 tespiti. Artificial Intelligence Studies, 2018; 1: 1–13.
• Li X., Geng M., Peng Y., Meng L., Lu S. Molecular immune pathogenesis and diagnosis of COVID-19. Journal of Pharmaceutical Analysis 2020; 10(1): 102‐108.
• Loey M., Manogaran G., Khalifa NEM. A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images. Neural Computing and Applications 2020; 1(26): 1-13.
• Narin A., Kaya C., Pamuk Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Anal Applic 2021; 24(1): 1207–1220.
• Ouchicha C., Ammor O., Meknassi M. CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images. Chaos, Solitons and Fractals 2020; 140(110245): 1-11.
• Ozturk T., Talo M., Yildirim EA., Baloglu UB., Yildirim O., Acharya UR. Automated detection of COVID-19 cases using deep neural networks with x-ray images. Computers in Biology and Medicine 2020; 121(103792): 1-11.
• Özbay E., Altunbey Özbay F. Derin öğrenme ve sınıflandırma yaklaşımları ile BT görüntülerinden Covid-19 tespiti. DÜMF Mühendislik Dergisi 2021; 12(2): 211-219.
• Sahinbas K., Catak FO. Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images (pp 451-466). Academic Press; 2021.
• Shuja J., Alanazi E., Alasmary W., Alashaikh A. COVID-19 open source data sets: a comprehensive survey. Appl Intell 2021; 51: 1296-1325.
• Simonyan K., Zisserman A. Very deep convolutional networks for large-scale image recognition. American Physiological Society Bethesda 2014; 1(6): 1-14.
• Singhal TA. Review of coronavirus disease-2019 (COVID-19). The Indian Journal of Pediatrics 2020; 87(1): 281–286.
• Tuncer T., Dogan S., Ozyurt F. An automated residual exemplar local binary pattern and iterative ReliefF based COVID-19 detection method using chest X-ray image. Chemometrics and Intelligent Laboratory Systems 2020; 203(104054): 1-11.
• Wang S., Kang B., Ma J., Zeng X., Xiao M., Guo J., Cai M., Yang J., Li Y., Meng X., Xu Bo. A deep learning algorithm using CT images to screen for Coronavirus disease (COVID-19). European Radiology 2021; 31(8): 6096-6104.
• Wang X., Peng Y., Lu L., Lu Z., Bagheri M., Summers RM. ChestX-ray8: Hospital-scale Chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017; 2097–2106.
• Xu X., Jiang X., Ma C., Du P., Li X., Lv S., Yu L., Ni Q., Chen Y., Su J., Lang G., Li Y., Zhao H., Liu J., Xu K., Ruan L., Sheng J., Qiu Y., Wu W., Li L. A Deep Learning system to screen novel coronavirus disease 2019. Pneumonia. Engineering 2020; 6(10): 1122–1129.
• Wang X., Peng Y., Lu L., Lu Z., Bagheri M., Summers RM. ChestX-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017; 2097–2106.