PneumoNet: Göğüs Röntgeni Görüntülerinden Derin Sinir Ağları Kullanarak Pnömoninin Otomatik Tespiti

Abstract

Pnömoni, akciğerlerdeki hava keseciklerinin şiddetli iltihaplanmasına neden olan tehlikeli bir hastalıktır. Dünya genelinde tüm yaş gruplarında yüksek morbidite ve mortaliteye sahip bulaşıcı hastalıklardan biridir. Göğüs röntgeni (CXR), düşük iyonize radyasyon dozu, düşük maliyeti ve kolay erişilebilirliği nedeniyle pnömoni teşhisinde yaygın olarak kullanılan bir teşhis ve görüntüleme yöntemidir. Çeşitli tıbbi uygulamalarda klinisyenlere CXR görüntülerinden pnömoni tespit ve teşhisinde yardımcı olmak için birçok derin öğrenme yöntemi önerilmiştir. CXR görüntülerini kullanarak pnömoniyi doğru bir şekilde tespit etmek için evrişimsel sinir ağı (ESA) kullanan yeni bir PneumoNet önerilmiştir. Doğal dil işleme (NLP) problemlerinde yüksek performans sağlayan dönüştürücü tabanlı derin öğrenme yöntemleri son zamanlarda araştırmacıların ilgisini çekmektedir. Bu çalışmada, CNN modelini kullanarak elde ettiğimiz sonuçlar dönüştürücü tabanlı mimarilerle karşılaştırılmıştır. Bu dönüştürücü mimariler görüntü dönüştürücü (ViT), kapılı çok katmanlı algılayıcı (gMLP), MLP-mikser ve FNet’tir. Bu çalışmada, modeli geliştirmek için kamu ve özel veri tabanlarından sağlıklı ve pnömoni CXR görüntüleri kullanılmıştır. Geliştirdiğimiz PneumoNet modeli, sağlıklı bireylerden pnömoniyi doğru bir şekilde tespit etmede özel ve kamu veri tabanları için sırasıyla %96,50 ve %94,29’luk en yüksek doğruluğu sağlamıştır.

Keywords

• Pnömoni tespiti
• tıbbi görüntü sınıflandırma
• göğüs röntgeni görüntüleme
• transformatör
• derin sinir ağları.

PneumoNet: Automated Detection of Pneumonia using Deep Neural Networks from Chest X-Ray Images

Abstract

Pneumonia is a dangerous disease that causes severe inflammation of the air sacs in the lungs. It is one of the infectious diseases with high morbidity and mortality in all age groups worldwide. Chest X-ray (CXR) is a diagnostic and imaging modality widely used in diagnosing pneumonia due to its low dose of ionizing radiation, low cost, and easy accessibility. Many deep learning methods have been proposed in various medical applications to assist clinicians in detecting and diagnosing pneumonia from CXR images. We have proposed a novel PneumoNet using a convolutional neural network (CNN) to detect pneumonia using CXR images accurately. Transformer-based deep learning methods, which have yielded high performance in natural language processing (NLP) problems, have recently attracted the attention of researchers. In this work, we have compared our results obtained using the CNN model with transformer-based architectures. These transformer architectures are vision transformer (ViT), gated multilayer perceptron (gMLP), MLP-mixer, and FNet. In this study, we have used the healthy and pneumonia CXR images from public and private databases to develop the model. Our developed PneumoNet model has yielded the highest accuracy of 96.50% and 94.29% for private and public databases, respectively, in detecting pneumonia accurately from healthy subjects.

Keywords

• Pneumonia detection
• medical image classification
• chest x-ray imaging
• transformer
• deep neural networks

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