Yapay Sinir Ağları Kullanılarak Pediatrik Akciğer Röntgen Görüntülerinden Pnömoni Tespiti

Year 2024, Volume: 27 Issue: 5, 1843 - 1852

Özgür Dündar , Sabri Koçer

https://doi.org/10.2339/politeknik.1332211

Abstract

Yapay Sinir Ağları Kullanılarak Pediatrik Akciğer Röntgen Görüntülerinden Pnömoni Tespiti
ÖZET
Tıbbi görüntüleme konusundaki çalışmalar son yıllarda önemli ölçüde artmıştır. Tıbbi görüntülemede yarı veya tam otomatik bölge tanıma sayesinde doktorlar teşhis için çok önemli bir kolaylığa sahiptir. Özellikle bu tür tıp uzmanlarının az olduğu ülkelerde, uzman bir doktor olmadan tedaviyi desteklemek çok önemlidir. Alveol olarak bilinen küçük hava kesecikleri, bir akciğer iltihabı olan pnömoniden en çok etkilenenlerdir. Hastaları iyileştirmek ve iltihabı ortadan kaldırırken zararı azaltmak için doğru tedavi koşullarını sağlamanın önemli bir bileşeni erken teşhis ve kesin tanıdır. X-ray cihazlarından elde edilen hasta fotoğraflarındaki gürültü ve bulanıklık, derin öğrenme algoritmaları ve görüntü işleme teknikleri kullanılarak temizlenmekte ve bu konuda oldukça yardımcı olmaktadır. Bu çalışmada, pnömonili pediatrik hastaların ve sağlıklı bireylerin göğüs röntgeni görüntüleri üzerinde çalışılmıştır. XGBoost (eXtreme gradient boosting) karar ağacına dayanan ve hesaplamalarında gradient boosting kullanan yenilikçi bir makine öğrenmesi algoritmasıdır. Yüksek sınıflandırma performansı ile %97,01 başarı elde etmiştir.
Anahtar Kelimeler: Tıbbi görüntüleme, Makine öğrenimi, Pediatrik Göğüs Röntgeni

Keywords

Tıbbi görüntüleme, Makine öğrenimi, Pediatrik Göğüs Röntgeni

Pneumonia Detection from Pediatric Lung X-Ray Images Using Artificial Neural Networks

Abstract

Pneumonia Detection from Pediatric Lung X-Ray Images Using Artificial Neural Networks
ABSTRACT
Studies on medical imaging have grown significantly in recent years. Doctors have a crucial convenience for diagnosis thanks to semi- or fully automatic region recognition in medical imaging. It is crucial to support treatment without a specialist doctor, particularly in those nations where there is a dearth of such medical professionals. The little air sacs known as alveoli are most impacted by pneumonia, a lung inflammation. A key component of providing the right therapy conditions to heal patients and reduce harm while eradicating inflammation is early detection and precise diagnosis. Noise and blurring in patient photos obtained from X-ray machines are cleaned using deep learning algorithms and image processing techniques, and they are very helpful in. In this study, we studied chest X-ray images of pediatric patients with pneumonia and healthy individuals. XGBoost (eXtreme gradient boosting) is an innovative machine learning algorithm based on decision tree and using gradient boosting in its computations. It achieved 97.01% success with high classification performance.
Keywords: Medical imaging, Machine learning, Pediatric Chest X-ray

Keywords

Medical imaging, Machine learning, Pediatric Chest X-ray

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