Derin Öğrenme Yöntemleri Kullanılarak BT Taramalarında Beyin Kanaması Teşhisinin Karşılaştırmalı Bir Analizi

Year 2023, Volume: 6 Issue: 1, 75 - 84, 15.03.2023

Tuğrul Hakan Gençtürk , Fidan Kaya Gülağız , İsmail Kaya

https://doi.org/10.38016/jista.1215025

Cited By: 2

Abstract

Teknolojinin gelişmesiyle birlikte yapay zekâ temelli uygulamalar pek çok alanda destek amaçlı kullanılmaktadır. Sağlık sektörü de bu tür uygulamaların yaygın kullanıldığı alanlardan bir tanesidir. Sağlık sektöründe teknolojik gelişime bağlı olarak meydana gelen bilgi artışı beraberinde radyolojik değerlendirmede uzmanlık gereğini doğurmuştur. Yoğun çalışma saatleri, sağlık kurumlarında her branştan uzmana ulaşılamaması ve özellikle acil patolojilerde erken teşhisin önemi göz önünde bulundurulduğunda hekimlere teşhis sürecinde destek olacak uygulamalara olan ihtiyacın önemi anlaşılmaktadır. Çalışma kapsamında Bilgisayarlı Tomografi (BT) görüntüleri kullanılarak beyin kanamalarının tespitini gerçekleştirmek amacıyla güncel derin öğrenme yöntemlerinden Görsel Geometri Grubu (VGG), Artık Sinir Ağı (ResNet) ve EfficientNet mimarileri yine güncel bir veri kümesi olan PhysioNet’e uygulanmıştır. Modeller doğruluk, kesinlik, hassasiyet ve F1 skor metrikleri kullanılarak hem kendi aralarında hem de literatürdeki çalışmalarla karşılaştırılmıştır. Gerçekleştirilen çalışma ile veri kümesine uygun model seçiminin önemi güncel modeller üzerinden ortaya konulmuştur. EfficientNet-B2 modelinin başarısı hem literatürdeki çalışmalardan hem de makale kapsamında değerlendirilen modellerden yüksek olmuştur. Elde edilen sonuçlar güncel derin öğrenme modellerinin, beyin kanaması teşhisine yardımcı olabilecek potansiyelde olduğunu göstermiştir. Çalışma acil servislerin yükünü çeken pratisyen hekimleri en azından beyin kanamasının varlığı konusunda uyarıp kanama durumunun gözden kaçmamasını sağlaması ve erken teşhisi açısından önem arz etmektedir.

Keywords

Beyin kanaması, BT, Derin Öğrenme, EfficientNet, VGG, ResNet

A Comparative Analysis of Brain Hemorrhage Diagnosis on CT Scans Using Deep Learning Methods

Abstract

With the development of technology, artificial intelligence-based applications are used for support in many areas. The health sector is one of the areas where such applications are widely used. The increase in knowledge in the health sector due to technological development has led to the need for expertise in radiological evaluation. Considering the intensive working hours, the inaccessibility of specialists from every branch in health institutions and the importance of early diagnosis, especially in emergency pathologies, the importance of the need for applications that will support physicians in the diagnosis process is understood. In the scope of the study, Visual Geometry Group (VGG), Residual Neural Network (ResNet) and EfficientNet architectures, which are among the current deep learning methods, were applied to PhysioNet, a recent dataset, in order to detect brain hemorrhages using Computed Tomography (CT) images. The models were compared among themselves and with existing studies in the literature using accuracy, precision, recall and F1 score metrics. With this study, the importance of choosing the appropriate model for the dataset has been demonstrated through current models. The success of the EfficientNet-B2 model was higher than both the studies in the literature and the models evaluated within the scope of the article. The results show that current deep-learning models have the potential to help in the diagnosis of an intracranial hemorrhage. The study is essential in terms of early diagnosis of intracranial hemorrhage by at least alerting general practitioners, who bear the burden of emergency services, to the presence of intracranial hemorrhage and ensuring that the bleeding condition is not overlooked.

Keywords

Brain Hemorrhage, CT, Deep Learning, EfficientNet, ResNet, VGG

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