COVID-19 Tespitinde Öğrenim Aktarımına CLAHE Tabanlı Geliştirme

Abstract

Derin öğrenme kullanılarak göğüs röntgeni görüntülerinin sınıflandırılması için öznitelik öğrenme ve gelişmiş ön işleme aşamaları üzerindeki geliştirmelerle COVID-19 hastalığının erken teşhisi mümkün hale getirmiştir. Bunun yanında, Derin Öğrenme popülaritesi nedeniyle birçok araştırmacı tarafından denenerek yüksek performanslı modeller ortaya sürülmüştür. Bu çalışmada göğüs röntgen filmlerine, AlexNet, MobileNet, VGG16 ve DarkNet19 gibi popüler derin öğrenme mimarilerindeki transfer öğrenme yaklaşımıyla sınıflandırmadan önce Kontrast Sınırlı Adaptif Histogram Eşitleme (CLAHE) kullanılarak ön işlem uygulanmıştır. Makalenin orijinalliği, göğüs röntgeni görüntülerini doğrudan ham veri ile eğitmek yerine önce hava yollarının ve patolojilerin daha belirgin yansımalarını elde edilerek gerçekleştirilmesidir. En başarılı CLAHE parametreleri çeşitli aralıklardaki deneyler sonucunda belirlenmiştir. Önerilen yaklaşımın diğer üstün katkısı, modelin eğitiminde ve testinde 3615 COVID-19'lu ve 3500 sağlıklı göğüs röntgeninden oluşan büyük ölçekli bir veri seti kullanılmasıdır. CLAHE tabanlı öğrenim aktarımı önerisi, en başarılı COVID-19 ve sağlıklı ikili sınıflandırma başarımına %95,878 doğruluk oranıyla VGG16 modeli üzerinde 56 disk değeri ve 0.2 klip limiti CLAHE parametrelerini kullanarak ulaşmıştır.

Keywords

• Derin Öğrenme
• Evrişimli sinir ağları
• Göğüs röntgeni
• Adaptif Histogram Eşitleme
• Tıbbi görüntü analizi

CLAHE based Enhancement to Transfer Learning in COVID-19 Detection

Abstract

Early diagnosis of COVID-19 disease becomes possible with the enhancements on feature learning and advanced pre-processing stages for classification of chest X-ray images using deep learning. Besides, high-performance models have been developed by many researchers due to the popularity of Deep Learning. In this study, chest X-ray images were pre-processed using Contrast Limited Adaptive Histogram Equalization (CLAHE) before the classification with particular popular transfer learning approaches in deep learning architectures including AlexNet, MobileNet, VGG16, and DarkNet19. The originality of the paper is pre-processing the images using CLAHE to obtain more significant representations of airways and pathologies instead of training with raw chest X-ray images. The best CLAHE parameters were determined considering the results of various trials at a specified range. The other superior contribution of the proposal is using a large-scale dataset, which is comprised of 3500 healthy and 3615 chest x-rays with COVID-19. The CLAHE-based transfer learning proposal achieved an accuracy rate of 95.878% as the most successful binary classification result for COVID-19 and healthy using VGG16 model and CLAHE parameters including disk value of 56, clip-limit of 0.2.

Keywords

• Deep Learning
• Convolutional neural networks
• chest x-ray
• Adaptive Histogram Equalization
• medical image analysis

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