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

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

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