Durmuş ÖZDEMİR, Naciye Nur ARSLAN

Göğüs Röntgeni Görüntüleri ile Covid-19 Hastalığının Erken Teşhisine Yönelik Derin Transfer Öğrenme Yöntemlerinin Analizi

Bu çalışmada, X-ray görüntüleri kullanılarak Covid-19 hastalığının erken teşhisini belirlemek için derin transfer öğrenme modellerinin analizinin sunulması amaçlanmıştır. Bu amaçla ImageNet yarışmasında başarılı olan VGG-16, VGG-19, Inception V3 ve Xception derin transfer öğrenme modelleri Covid-19 hastalığının tespiti için kullanılmıştır. Ayrıca eğitim verileri için 280 göğüs röntgeni görüntüsü ve test verileri için 140 göğüs röntgeni görüntüsü kullanılmıştır. İstatistiksel analiz sonucunda en başarılı modelin Inception V3 (%92), sonraki başarılı modelin Xception (%91) olduğu ve VGG-16 ve VGG-19 modellerinin de aynı sonucu verdiği görülmüştür (%88). Covid-19 hastalığı teşhisi için önerilen derin öğrenme modelleri, test maliyetleri, test doğruluk oranı, personel iş yükü ve test sonuçları bekleme süresi gibi covid-19 hastalığı sorunlarının teşhisinde önemli avantajlar sunmaktadır.

Anahtar Kelimeler:

Biyomedikal bilişim, Derin öğrenme, Covid-19 teşhisi, Görüntü sınıflandırma

Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images

This study aimed to present an analysis of deep transfer learning models to support the early diagnosis of Covid-19 disease using X-ray images. For this purpose, the deep transfer learning models VGG-16, VGG-19, Inception V3 and Xception, which were successful in the ImageNet competition, were used to detect Covid-19 disease. Also, 280 chest x-ray images were used for the training data, and 140 chest x-ray images were used for the test data. As a result of the statistical analysis, the most successful model was Inception V3 (%92), the next successful model was Xception (%91), and the VGG-16 and VGG-19 models gave the same result (%88). The proposed deep learning model offers significant advantages in diagnosing covid-19 disease issues such as test costs, test accuracy rate, staff workload, and waiting time for test results.

Keywords:

Biomedical Informatics, Deep Learning, Covid-19 Diagnosis, Image Classification,

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