COVID-19 ve Zatürre Tespiti için İkili ve Çok Sınıflı Göğüs Röntgeni Sınıflandırması

Öz

Zatürre ve COVID-19 gibi hastalıkları içeren akciğer bozuklukları, her yıl milyonlarca insanı etkileyerek küresel sağlık için önemli zorluklar oluşturmaktadır. Bu çalışma, zatürre, COVID-19 ve sağlıklı bireylerden elde edilen göğüs röntgeni görüntülerinin otomatik sınıflandırılması için özellikle AlexNet ve SqueezeNet gibi gelişmiş derin öğrenme mimarilerinin uygulanmasını incelemektedir. SqueezeNet ile ikili sınıflandırma görevlerinde %99.85 gibi etkileyici bir doğruluk, çok sınıflı sınıflandırma için ise %97.72 doğruluk elde edilmiştir. Sonuçlar, özellikle COVID-19 ile zatürreyi ayırt etmede, SqueezeNet’in duyarlılık, özgüllük ve doğruluk açısından AlexNet’ten daha iyi performans gösterdiğini ortaya koymaktadır. Bu durum, SqueezeNet’in hızlı tanı uygulamalarındaki hesaplama verimliliğini ve etkinliğini vurgulamaktadır. Bulgularımız, özellikle kaynakların kısıtlı olduğu ortamlarda, zamanında tanının hasta sonuçlarını iyileştirmedeki önemini ortaya koymaktadır. Bilgisayar destekli tanı (CAD) teknolojilerinin erken tespit ve uygun tedaviye katkı sağlayabileceği değerlendirilmektedir. Gelecek çalışmalar, heterojen ve dengesiz veri kümeleri üzerinde derin sinir ağı modellerini araştıracak ve bu yöntemleri BT görüntülerine uygulayarak genellenebilirliği artırmayı hedefleyecektir.

Anahtar Kelimeler

• COVID-19
• Koronavirüs
• Zatürre
• Röntgen görüntüleme
• derin öğrenme

Binary and Multi-Class Chest X-Ray Classification for COVID-19 and Pneumonia Detection

Öz

The Lung disorders, encompassing conditions such as pneumonia and COVID-19, represent significant challenges to global health, impacting millions annually. This study investigates the application of advanced deep learning architectures, specifically AlexNet and SqueezeNet, for the automated classification of chest X-ray (CXR) images from patients diagnosed with pneumonia, COVID-19, and healthy individuals. We achieved an impressive accuracy of 99.85% in binary classification tasks with SqueezeNet and 97.72% for multi-class classification. The results indicate that SqueezeNet outperformed AlexNet in sensitivity, specificity, and accuracy, particularly in distinguishing between COVID-19 and pneumonia. This highlights SqueezeNet's computational efficiency and effectiveness in rapid diagnostic applications. Our findings underscore the importance of timely diagnosis in improving outcomes, especially in resource-limited settings. The use of computer-aided diagnosis (CAD) technologies can aid in early detection and appropriate treatment. Future work will explore deep neural network models on heterogeneous and unbalanced datasets and apply these methods to CT images to enhance generalizability.

Anahtar Kelimeler

• COVID-19
• Coronavirus
• Pneumonia
• X-ray imaging
• deep learning

Kaynakça

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