Support Vector Machine Fed with Different Deep Features for Classification of Lung Diseases from Chest X-Ray Images

Year 2024, , 11 - 21, 31.01.2024

Hüseyin Üzen , Hüseyin Fırat

https://doi.org/10.17671/gazibtd.1366846

Abstract

COVID-19, tuberculosis, and pneumonia, three of the most deadly lung diseases, are routinely detected using chest X-ray (CXR) scans. Recent technological advancements are ushering in a new era of computer-assisted systems for automated diagnosis, offering significant benefits. This study proposes a three-stage deep learning model designed to differentiate these diseases from CXRs. In the initial phase of the model, a Convolutional Neural Network (CNN) is used to extract deep features, including depthwise separable convolution, conventional convolution, and fully connected layers. In the second phase, a Support Vector Machine (SVM) classifier is employed for retraining to achieve higher classification accuracy, maximizing the utilization of deep features from different layers. The third stage involves testing the model. Experimental tests were conducted on a CXR dataset comprising four classes: COVID-19, Pneumonia, Normal, and Tuberculosis. Following comprehensive experimental studies, the proposed model achieved an average accuracy of 99.30%. Moreover, in class-specific results, it reached 100% accuracy for COVID-19 and Tuberculosis, and 98.60% accuracy for Normal and Pneumonia cases, indicating the high effectiveness of the proposed model in classifying COVID-19 and Tuberculosis. Furthermore, in the second part of the experimental studies, the outcomes of the proposed model were compared with existing models, demonstrating superior achievements.

Keywords

COVID-19, pneumonia, tuberculosis, deep learning, support vector machine

Göğüs Röntgeni Görüntülerinden Akciğer Hastalıklarının Sınıflandırılması için Farklı Derin Öznitelikler ile Beslenen Destek Vektör Makinesi

Abstract

En ölümcül akciğer hastalıklarından üçü olan COVID-19, tüberküloz ve zatürre, rutin olarak göğüs röntgeni (GR) taramaları kullanılarak tespit edilmektedir. Son teknolojik gelişmeler, otomatik teşhis için bilgisayar destekli sistemlerde yeni bir çağ başlatmakta ve önemli faydalar sunmaktadır. Bu çalışma, bu hastalıkları GR'lerden ayırt etmek için tasarlanmış üç aşamalı yeni bir derin öğrenme modeli önermektedir. Modelin ilk aşamasında, derinlemesine ayrılabilir evrişim, geleneksel evrişim ve tam bağlı katmanlar dahil olmak üzere derin özellikleri çıkarmak için bir Evrişimsel Sinir Ağı (ESA) kullanılmaktadır. İkinci aşamada, daha yüksek sınıflandırma başarısı elde etmek için Destek Vektör Makineleri (DVM) sınıflandırıcısı kullanılarak tekrar bir eğitim sürecinden geçirilmektedir. Bu sayede farklı katmanlardan alınan derin özelliklerden daha fazla yararlanılmaktadır. Üçüncü aşamada ise model test edilmektedir. Deneysel çalışmalarda dört sınıftan oluşan GR veri kümesi üzerinde testler gerçekleştirilmiştir. Bu veri kümesi COVID-19, Pnömoni, Normal ve Tüberküloz sınıflarını içermektedir. Kapsamlı deneysel çalışmalar sonucunda önerilen model %99,30 ortalama doğruluk sonucuna ulaşmıştır. Diğer yandan sınıf bazlı sonuçlarda COVID-19 ve Tüberküloz için %100, Normal ve Pnömoni vakaları için ise %98,60 doğruluk oranına ulaşmıştır. Bu sonuçlar COVID-19 ve Tüberküloz sınıflandırması için önerilen modelin çok etkili olduğu görülmektedir. Ayrıca deneysel çalışmaların ikinci bölümünde, önerilen model sonuçları, mevcut modeller ile karşılaştırılmış ve üstün başarılar elde ettiği görülmüştür.

Keywords

COVID19, pnömoni, tüberküloz, derin öğrenme, destek vektör makinesi

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