An integrated diagnosis system based on pretrained deep convolutional neural networks for Otitis media

Year 2019, Volume: 8 Issue: 4, 1498 - 1511, 24.12.2019

Zafer Cömert

https://doi.org/10.17798/bitlisfen.600636

Cited By: 6

Abstract

Otitis media
(OM) is a medical concept representing a range of inflammatory middle ear
disorders. OM is one of the most common diseases worldwide, especially in
childhood. In clinical practice, the diagnosis of OM is carried out by
examining the images of the middle ear obtained via the otoscope device by
specialists. The subjective examination leads to arise the variabilities among
observers. At the same time, the use of computer-aided systems in this area is
not common enough. Failure to diagnose OM disorders in a timely manner leads to
the progression of the diseases, the emergence of hearing, speech, and
cognitive disorders. To overcome all these disadvantages, an integrated
diagnostic system based on the pretrained deep convolutional neural networks is
proposed for the diagnosis of OM in this study. Experimental studies were
carried out on 898 otoscope images, representing five different classes,
collected from volunteer patients admitted to Özel Van Akdamar Hospital. As a
result, the proposed model achieved 82.16% classification success. With the
end-to-end learning and high sensitivity provided by the proposed model based
on convolutional neural networks, OM diagnosis can be realized objectively and
physicians' decision-making process can be supported using this system. The
proposed method has produced promising results in these respects.

Keywords

Biomedical signal processing, Diagnosis system, Otitis media, Convolutional neural networks

Otitis media için evrişimsel sinir ağlarına dayalı entegre bir tanı sistemi

Abstract

Otitis media (OM) bir dizi iltihaplı
orta kulak rahatsızlıklarını temsil eden tıbbi bir kavramdır. OM dünya
genelinde, özellikle çocukluk çağında, görülen en yaygın hastalıklardan
biridir. Klinik pratikte OM tanısı, otoskop cihazıyla elde edilen orta kulak
görüntüsünün kulak buran boğaz uzmanları tarafından incelenmesiyle
gerçekleştirilir. İncelemenin sübjektif olarak yapılması, gözlemciler arasında değişkenliklerin
ortaya çıkmasına neden olmaktadır. Aynı zamanda, bu alanda bilgisayar destekli
sistemlerinin kullanımının da yeteri kadar yaygın olmadığı görülmektedir. OM
rahatsızlıklarının zamanında teşhis edilememesi, hastalıkların ilerlemesine ve
buna bağlı olarak da işitme, konuşma ve bilişsel rahatsızlıkların ortaya
çıkmasına neden olmaktadır. Tüm bu dezavantajların üstesinden gelmek üzere, bu
çalışmada OM teşhisi için önceden eğitilmiş evrişimsel sinir ağlarına dayalı
entegre bir tanı sistemi önerilmiştir. Deneysel çalışmalar, Özel Van Akdamar
Hastanesinde gönüllü hastalardan toplanan ve toplamda beş farklı sınıfı temsil
eden 898 adet otoskop imgeleri üzerinde gerçekleştirilmiştir. Sonuç olarak,
önerilen model %82.16 sınıflandırma başarısı sağlanmıştır. Evrişimsel sinir ağlarına
dayalı önerilen modelin sağladığı uçtan uca öğrenme ve yüksek hassasiyetle, OM
teşhisinin objektif bir şekilde yapılabilmesi ve tanı sürecinde hekimlerin karar
verme sürecinin desteklenmesi sağlanabilir. Önerilen yöntem bu açılardan umut
verici sonuçlar üretmiştir.

Keywords

Biyomedikal işaret işleme, Tanı sistemi, Otitis media, Evrişimsel sinir ağları

Thanks

Verilerin toplanması sırasında harcadığı yoğun emekten dolayı Erdal BAŞARAN’a ve araştırmaya yön veren katkılarından dolayı Dr. Yüksel ÇELİK’e, ayrıca CTG Araştırma Gurubunun tüm üyelerine teşekkür ederim.

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