A Vision Transformer-based Approach for Automatic COVID-19 Diagnosis on Chest X-ray Images

Year 2023, Volume: 13 Issue: 2, 778 - 791, 01.06.2023

Ishak Pacal

https://doi.org/10.21597/jist.1225156

Cited By: 5

Abstract

The new type of coronavirus disease (COVID-19), which has emerged in recent years, has become a serious disease that threatens health worldwide. COVID-19, which can be transmitted very quickly and with serious increases in death, has paved the way for many concerns. With the spread of the epidemic to a universal dimension, many studies have been carried out for the early diagnosis of this disease. With early diagnosis, both fatal cases are prevented and the planning of the epidemic can be easier. The fact that X-ışını images are much more advantageous than other imaging techniques in terms of time and applicability, and also that they are economical, has led to the focus of early diagnosis-based applications and methods on these images. Deep learning approaches have had a great impact in the diagnosis of COVID-19, as in the diagnosis of many diseases. In this study, we propose a diagnostic system based on the transformer method, which is the most up-to-date and much more popular architecture than previous techniques of deep learning such as CNN-based approaches. This method includes an approach based on vision transformer models and a more effective diagnosis of COVID-19 disease on a new dataset, the COVID-QU-Ex dataset. In experimental studies, it has been observed that vision transformer models are more successful than CNN models. In addition, the ViT-L16 model showed a much higher performance compared to similar studies in the literature, providing test accuracy and F1-score of over 96%.

Keywords

COVID-19, COVID-19 detection, Vision transformers, Lung disease detection, Deep learning

Göğüs Röntgeni Görüntülerinden Otomatik COVID-19 Teşhisi için Görü Transformatörüne Dayalı Bir Yaklaşım

Abstract

Son yıllarda ortaya çıkan yeni tip Koronavirüs hastalığı (COVID-19), dünya çapında sağlığı tehdit eden ciddi bir hastalık olmuştur. COVID-19 çok hızlı bir şekilde bulaşabilen ve ciddi ölüm artışları ile birçok endişeye zemin hazırlamıştır. Salgının evrensel boyuta taşınmasıyla bu hastalığın erken teşhisine yönelik birçok çalışma yapılmıştır. Erken teşhis ile hem ölümcül vakaların önüne geçilmiş olunmakta hem de salgının planlanması daha kolay olabilmektedir. X-ışını görüntülerinin zaman ve uygulanabilirlik açısından diğer görüntüleme tekniklerine nazaran çok daha avantajlı olması ve ayrıca ekonomik olması erken teşhis bazlı uygulama ve yöntemlerin bu görüntülerin üzerine yoğunlaşmasına neden olmuştur. Derin öğrenme yaklaşımları birçok hastalık teşhisinde olduğu gibi COVID-19 teşhisinde de çok büyük bir etki oluşturmuştur. Bu çalışmada, derin öğrenmenin CNN tabanlı yaklaşımları gibi daha önceki tekniklerinden ziyade en güncel ve çok daha popüler bir mimarisi olan transformatör yöntemine dayalı bir teşhis sistemi önerdik. Bu sistem, görü transformatör modelleri temelli bir yaklaşım ve yeni bir veri seti olan COVID-QU-Ex üzerinde COVID-19 hastalığının daha efektif bir teşhisini içermektedir. Deneysel çalışmalarda, görü transformatör modellerinin CNN modellerinden daha başarılı olduğu gözlemlenmiştir. Ayrıca, ViT-L16 modeli %96’nın üzerinde test doğruluğu ve F1-skoru sunarak, literatürde benzer çalışmalara kıyasla çok daha yüksek bir başarım göstermiştir.

Keywords

COVID-19, COVID-19 tespiti, Görü transformatörler, Akciğer hastalık tespiti, Derin Öğrenme

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