Derin Sinir Ağlarını Kullanan Göğüs Röntgenleri ile Otomatik Tüberküloz Sınıflandırması Örnek Çalışma: Nijerya Halk Sağlığı

Year 2024, Volume: 19 Issue: 1, 55 - 64, 28.03.2024

Muhammad Zaharaddeen Abubakar Mustafa Kaya , Mustafa Eriş , Mohammed Mansur Abubakar , Serkan Karakuş , Khalid Jibril Sani

https://doi.org/10.55525/tjst.1222836

Cited By: 1

Abstract

Bulaşıcı bir akciğer rahatsızlığı olan tüberküloz, önde gelen küresel ölüm faktörü olarak karşımıza çıkıyor. Nijerya'da halk sağlığı üzerindeki önemli etkisi, kapsamlı müdahale stratejilerini gerektirmektedir. Bu hastalığın tespit edilmesi, önlenmesi ve tedavi edilmesi hâlâ zorunludur. Tanı araçları arasında göğüs röntgeni (CXR) görüntüleri çok önemli bir role sahiptir. Derin öğrenmedeki son gelişmeler, tıbbi görüntü analizini önemli ölçüde iyileştirdi. Bu araştırmada, sağlam modeller oluşturmak için kamuya açık ve tescilli CXR görüntü veri kümelerinden yararlandık. Önceden eğitilmiş derin sinir ağlarından yararlanarak tüberküloz tespitini geliştirmeyi hedefledik. Etkileyici bir şekilde, deneylerimiz dikkate değer sonuçlar verdi. Özellikle, ilgili açık ve özel veri setlerinde %98 ve %86'lık f1 puanlarına ulaşıldı. Bu sonuçlar, CXR görüntülerinden tüberkülozun etkili bir şekilde tanımlanmasında derin sinir ağlarının gücünün altını çiziyor. Çalışma, bu teknolojinin hastalığın yaygın etkisiyle mücadelede umut vaat ettiğini belirliyor.

Keywords

Derin öğrenme, Tüberküloz, ResNet, MobileNet, Veri arttırma

Automated Tuberculosis Classification with Chest X-Rays Using Deep Neural Networks -Case Study: Nigerian Public Health

Abstract

Tuberculosis, a contagious lung ailment, stands as a prominent global mortality factor. Its significant impact on public health in Nigeria necessitates comprehensive intervention strategies. Detecting, preventing, and treating this disease remains imperative. Chest X-ray (CXR) images hold a pivotal role among diagnostic tools. Recent strides in deep learning have notably improved medical image analysis. In this research, we harnessed publicly available and proprietary CXR image datasets to construct robust models. Leveraging pre-trained deep neural networks, we aimed to enhance tuberculosis detection. Impressively, our experimentation yielded remarkable outcomes. Notably, f1-scores of 98% and 86% were attained on the respective public and private datasets. These results underscore the potency of deep neural networks in effectively identifying tuberculosis from CXR images. The study emphasizes the promise of this technology in combating the disease's spread and impact.

Keywords

Deep Learning, Tuberclosis, ResNet, MobileNet, Data Augmentation

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