3BResNet: A Novel Residual Block-Based ResNet Model Approach for COVID19 Detection

Year 2023, , 925 - 940, 28.09.2023

Ekrem Eşref Kılınç , Fahrettin Aka , Sedat Metlek

https://doi.org/10.17798/bitlisfen.1346730

Cited By: 1

Abstract

In recent years, upper respiratory tract infections that have affected the whole world have caused the death of millions of people. It is predicted that similar infections may occur in the coming years. Therefore, it is necessary to develop methods that can be used widely, especially during epidemic periods. The study developed a decision support system for use in upper respiratory tract infections. At this stage, first, the ResNet models in the literature were examined and an application was developed on the SARS-CoV-2 Ct dataset. Next stage, the block structure in the ResNet models in the literature was changed, the number of layers was reduced, and a new model was proposed that provides higher success with fewer parameters. With the proposed model, the values 0.97, 0.97, 0.94, and 0.98 were achieved for accuracy, F1 score, precision and sensitivity on the SARS-CoV-2 Ct dataset, respectively. When the obtained values are compared to state of the art methods in the literature, it has been determined that they are at a competitive level with much fewer parameters. Hardware-related problems encountered in the training of ResNet models at low hardware levels were solved with the proposed model, resulting in a higher success rate. Furthermore, the proposed model can be widely used in different decision support systems that are urgently needed in adverse conditions such as pandemics due to its lightweight structure and high-performance results.

Keywords

Deep neural network, Residual block, ResNet, 3BResNet.

3BResNet: COVID19 Tespiti için Yeni Bir Artık Blok Tabanlı ResNet Modeli Yaklaşımı

Abstract

Son yıllarda tüm dünyayı etkisi altına alan üst solunum yolu enfeksiyonları milyonlarca insanın ölümüne neden olmuştur. Önümüzdeki yıllarda da benzer enfeksiyonların yaşanabileceği öngörülmektedir. Bu nedenle özellikle salgın dönemlerinde yaygın olarak kullanılabilecek yöntemlerin geliştirilmesi gerekmektedir. Çalışmada üst solunum yolu enfeksiyonlarında kullanılmak üzere bir karar destek sistemi geliştirilmiştir. Bu aşamada öncelikle literatürde yer alan ResNet modelleri incelenmiş ve SARS-CoV-2 Ct veri seti üzerinde bir uygulama geliştirilmiştir. Sonraki aşamada literatürdeki ResNet modellerindeki blok yapısı değiştirilmiş, katman sayısı azaltılmış ve daha az parametre ile daha yüksek başarı sağlayan yeni bir model önerilmiştir. Önerilen model ile SARS-CoV-2 Ct veri kümesi üzerinde doğruluk, F1 skoru, hassasiyet ve duyarlılık için sırasıyla 0.97, 0.97, 0.94 ve 0.98 değerleri elde edilmiştir. Elde edilen değerler literatürdeki son teknoloji yöntemlerle kıyaslandığında çok daha az parametre ile rekabet edebilir düzeyde olduğu tespit edilmiştir. ResNet modellerinin düşük donanım seviyelerinde eğitilmesinde karşılaşılan donanım kaynaklı sorunlar önerilen model ile çözülerek daha yüksek başarı oranı elde edilmiştir. Ayrıca önerilen model, hafif yapısı ve yüksek performanslı sonuçları nedeniyle pandemi gibi olumsuz koşullarda acil ihtiyaç duyulan farklı karar destek sistemlerinde yaygın olarak kullanılabilecektir.

Keywords

Derin sinir ağı, Artık blok, ResNet, 3BResNet

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