Comparison of Deep Learning Models in Carotid Artery Intima-Media Thickness Ultrasound Images: CAIMTUSNet

Year 2022, , 1 - 12, 31.01.2022

Serkan Savaş , Nurettin Topaloğlu , Ömer Kazcı Pınar Koşar

https://doi.org/10.17671/gazibtd.804617

Cited By: 9

Abstract

Deep learning is a machine learning technique that uses deep neural networks, which are multilayer neural networks that contain two or more hidden layers. In recent years, deep learning algorithms are also used to solve machine learning problems in medicine. Carotid artery disease is a type of cardiovascular disease that can result in a stroke. If a stroke is not diagnosed early, it is in the first place among the disabling diseases and the third place for the most common cause of death after cancer and heart disease. In this study, the classification performances of deep learning architectures in the biomedical field are compared, and Carotid Artery (CA) Intima-Media Thickness (IMT) Ultrasound (US) images were used. For an early diagnosis, AlexNet, ZFNet, VGGNet (16-19), which had successful results in the ImageNet competition, and authors’ original CNNcc models were used for comparison. An image database of CA-IMT-US which contains 501 ultrasound images from 153 patients was used to test the models' classification performances. It is seen that AlexNet, ZFNet, VGG16, VGG19, and CNNcc models achieved rates of 91%, 89.1%, 93%, 90%, and 89.1% respectively. The CNNcc model was found to produce successful classification results on CAIMTUS images when different performance indicators are also taken into account. In addition, different performance indicators including confusion matrices were investigated and the results were announced. The results showed that deep architectures are promising in the biomedical field and can provide proper classification on biomedical images so; this can help clinics to diagnose the disease early.

Keywords

Deep learning, image processing, carotid artery, intima-media thickness, convolutional neural network, machine learning

Thanks

The authors would like to thank the Radiology Department of Ankara Training and Research Hospital for their kindly cooperation and providing all the ultrasound images used.

Karotis Arter Intima-Medya Kalınlığı Ultrason Görüntülerinde Derin Öğrenme Modellerinin Karşılaştırılması: CAIMTUSNet

Abstract

Derin öğrenme, iki veya daha fazla gizli katman içeren çok katmanlı sinir ağları olan derin sinir ağlarını kullanan bir makine öğrenimi tekniğidir. Son yıllarda tıpta makine öğrenimi problemlerini çözmek için derin öğrenme algoritmaları da kullanılmaktadır. Karotis arter hastalığı, felçle sonuçlanabilen bir tür kardiyovasküler hastalıktır. İnme erken teşhis edilmezse, sakatlayıcı hastalıklar arasında ilk sırada, kanser ve kalp hastalıklarından sonra en sık ölüm nedeni olarak üçüncü sırada yer almaktadır. Bu çalışmada, derin öğrenme mimarilerinin biyomedikal alandaki sınıflandırma performansları karşılaştırılmış ve Karotis Arter (KA) Intima Media Thickness (IMT) Ultrason (US) görüntüleri kullanılmıştır. Erken teşhis için, ImageNet yarışmasında başarılı sonuçlar alan AlexNet, ZFNet, VGGNet (16-19) ve karşılaştırma için yazarların özgün CNNcc modelleri kullanılmıştır. 153 hastadan 501 US görüntüsünü içeren bir KA-IMT-US görüntü veritabanı, modellerin sınıflandırma performanslarını test etmek için kullanılmıştır. AlexNet, ZFNet, VGG16, VGG19 ve CNNcc modellerinin sırasıyla %91,%89.1, %93, %90 ve %89.1 oranlarına ulaştığı görülmüştür. CNNcc modelinin, farklı performans göstergeleri de hesaba katıldığında KAIMTUS görüntüleri üzerinde başarılı sınıflandırma sonuçları ürettiği bulunmuştur. Ayrıca çalışmada karışıklık matrislerini de içeren farklı performans göstergeleri incelenmiş ve sonuçlar açıklanmıştır. Sonuçlar, derin mimarilerin biyomedikal alanda ümit verici olduğunu ve biyomedikal görüntülerde uygun sınıflandırma sağlayabileceğini göstermiştir ki bu, kliniklerin hastalıkları erken teşhis etmesine yardımcı olabilir.

Keywords

Derin öğrenme, karotis arter, görüntü işleme, Karotis arter, intima-media kalınlığı, evrişimli sinir ağları, makine öğrenmesi

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