AI-powered diagnosis of respiratory diseases: Evaluating vision transformers and ResNet architectures for covid-19 and lung pathologies

Year 2025, Volume: 14 Issue: 4

Ahmet Solak

Abstract

This study systematically evaluates the efficacy of advanced deep learning architectures, namely Vision Transformers (ViT) and various ResNet models (ResNet50, ResNet101, ResNet152), in the classification of chest radiographs into four clinically significant diagnostic categories: Normal, Lung Opacity, Viral Pneumonia, and COVID-19. A meticulously curated dataset comprising 21,165 chest X-ray images was utilized to benchmark the models' performance across key evaluation metrics, including precision, recall, F1-score and accuracy. The experimental evaluation reveals that ViT model achieved 90.25% accuracy, 91.56% precision, 89.22% recall, and a 90.25% F1-score. These findings highlight the potential of AI-driven approaches in augmenting medical diagnostics, improving diagnostic accuracy, and enhancing healthcare delivery, particularly in resource-limited settings. The study underscores the applicability of Vision Transformers in complex medical imaging tasks and contributes to the growing body of research supporting AI-based solutions for respiratory diseases and other healthcare challenges.

Keywords

Chest radiographs , COVID-19 diagnosis , Deep learning , ResNet , Vision transformer

Solunum hastalıklarının yapay zeka destekli teşhisi: Covid-19 ve akciğer patolojileri için görü dönüştürücüleri ve ResNet mimarilerinin değerlendirilmesi

Abstract

Bu çalışma, gelişmiş derin öğrenme mimarilerinin – özellikle Vision Transformers (ViT) ve çeşitli ResNet modellerinin (ResNet50, ResNet101, ResNet152) – göğüs röntgenlerini Normal, Akciğer Opasitesi, Viral Pnömoni ve COVID-19 olmak üzere dört klinik açıdan önemli tanısal kategoriye sınıflandırmadaki etkinliğini sistematik olarak değerlendirmektedir. Modellerin performansını, hassasiyet, geri çağırma, F1-skora ve doğruluk gibi temel değerlendirme metrikleri üzerinden ölçmek amacıyla özenle hazırlanmış 21.165 göğüs X-ışını görüntüsünden oluşan bir veri seti kullanılmıştır. Deneysel değerlendirmeler, ViT modelinin %90.25 doğruluk, %91.56 hassasiyet, %89.22 geri çağırma ve %90.25 F1-skora elde ettiğini ortaya koymaktadır. Bu bulgular, yapay zeka temelli yaklaşımların tıbbi tanı süreçlerini güçlendirme, tanı doğruluğunu artırma ve özellikle kaynak kısıtlı ortamlarda sağlık hizmetlerinin sunumunu iyileştirme potansiyeline işaret etmektedir. Çalışma, karmaşık tıbbi görüntüleme görevlerinde Vision Transformers'ın uygulanabilirliğini vurgulamakta ve solunum yolu hastalıkları ile diğer sağlık sorunlarına yönelik yapay zeka temelli çözümleri destekleyen artan araştırma literatürüne katkıda bulunmaktadır.

Keywords

: Göğüs röntgenleri , COVID-19 tanısı , Derin öğrenme , ResNet , Vision transformer

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