Göğüs röntgenlerinde pnömoniyi tespit etmek için derin öğrenme yaklaşımı

Abstract

Pnömoni her yıl çok sayıda çocuğun ölümüne neden olmakta ve dünya nüfusunun belli bir oranını oluşturmaktadır. Göğüs röntgenleri öncelikle bu hastalığı teşhis etmek için kullanılır, ancak eğitimli bir radyolog için bile göğüs röntgenlerini yorumlamak kolay değildir. Bu çalışmada, radyologlara karar verme süreçlerinde yardımcı olmak için dijital göğüs röntgeni görüntüleri üzerinde eğitilmiş bir pnömoni tespiti modeli sunulmaktadır. Çalışma, Phyton platformunda son zamanlarda yaygın olarak tercih edilen derin öğrenme modelleri kullanılarak gerçekleştirilmiştir. Bu çalışmada, dört farklı CNN modeli ile pnömoni sınıflandırması için bir derin öğrenme çerçevesi önerilmiştir. Bunlardan üçü önceden eğitilmiş modeller, MobileNet, ResNet ve AlexNet, diğeri ise önerilen CNN modelidir. Bu modeller performanslarına göre birbirleriyle karşılaştırılarak değerlendirilmektedir. Önerilen derin öğrenme çerçevesinin deneysel performansı, kesinlik, duyarlılık ve F1-puanı temelinde değerlendirilir. Modeller sırasıyla %93, %97, %97 ve %86 doğruluk değerlerine ulaşmıştır. Önerilen ResNet modelinin diğerlerine kıyasla en yüksek sonuçları elde ettiği açıktır.

Keywords

• ANN
• Derin Öğrenme
• Python
• Zatürre
• X-Ray

A deep learning approach for detecting pneumonia in chest X-rays

Abstract

Pneumonia causes the death of many children every year and constitutes a certain proportion of the world population. Chest X-rays are primarily used to diagnose this disease, but even for a trained radiologist, chest X-rays are not easy to interpret. In this study, a model for pneumonia detection trained on digital chest X-ray images is presented to assist radiologists in their decision-making processes. The study is carried out on the Phyton platform by using deep learning models, which have been widely preferred recently. In this study, a deep learning framework for pneumonia classification with four different CNN models is proposed. Three of them are pre-trained models, MobileNet, ResNet and AlexNet and the other is the recommended CNN Model. These models are evaluated by comparing them with each other according to their performance. The experimental performance of the proposed deep learning framework is evaluated on the basis of precision, recall and f1-score. The models achieved accuracy values of 93%, 97%, 97% and 86%, respectively. It is clear that the proposed ResNet model achieves the highest results compared to the others.

Keywords

• CNN
• Deep Learning
• Python
• Pneumonia
• X-ray

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