Derin öğrenme tabanlı topluluk sınıflandırıcı yaklaşımı ile gastrointestinal anomalilerin tespiti

Öz

Gastrointestinal bölgede yer alan anomalilerin teşhisi güncel bir araştırma alanıdır. Bu bölgenin incelenmesi için kablosuz kapsül endoskopi (WCE), geleneksel endoskopinin risklerini önlemek ve ağrısız bir süreç sağlamak amacıyla tercih edilen alternatif bir teknolojidir. Fakat birçok avantaja sahip bu teknoloji, düşük çerçeve yoğunluğu sunmaktadır. Verilerin kalitesini etkileyen bu durum, teşhis doğruluk oranının düşmesine neden olmaktadır. Bu çalışmada KID Atlas Veri kümesi 2’den elde edilen WCE endoskopi görüntüleri kullanılmış ve gatrointestinal bölgedeki inflammatory anomali, vascular anomali, polypoid anomali ve normal görüntü kategorilerinin tespiti için üç aşamalı yapay zeka destekli bir tanı süreci geliştirilmiştir. İlk aşama için 5 farklı yaklaşım kullanılarak görüntüler üzerindeki kritik noktalar belirginleştirilmiştir. İyileştirilen bu görüntüler, bölge öneri temelli bir nesne tanıma algoritması ile sınıflandırılmıştır ve kullanılan yaklaşımlara göre performans karşılaştırması yapılmıştır. İkinci aşamada, ilk aşamada maksimum performans gösteren iyileştirilmiş verilere görüntü çoğaltma tekniği uygulanmıştır. Böylece dengeli ve yeterli sayıda görüntü içeren bir veri kümesi oluşturulmuştur. Üçüncü aşamada bu güncel veri kümesi beş ayrı nesne tanıma algoritması ile sınıflandırılmıştır. Ancak her bir algoritmanın sahip olduğu bireysel başarı farklıdır. Bu nedenle her bir kategori için kararlı çıktılar elde etmek ve kategoriler arasında dengeli bir tespit süreci oluşturmak için topluluk öğrenme yaklaşımı kullanılmıştır. Son olarak inşa edilen bu hibrit yapı ile kategoriler arasında dengeli ve kararlı bir tahmin işlevi sağlanmıştır.

Anahtar Kelimeler

• Gastrointestinal anomaliler
• Kablosuz kapsül endoskopi
• Derin öğrenme
• Topluluk öğrenme

Detection of gastrointestinal anomalies with a deep learning-based ensemble classifier approach

Abstract

Diagnosis of anomalies in the gastrointestinal tract is a current research area. Wireless capsule endoscopy (WCE) for the evaluation of this region is a preferred alternative technology to avoid the risks of traditional endoscopy and to provide a painless process. But this technology, which has many advantages, offers low frame density. This situation affects the quality of the data and causes to a decrease in the diagnostic accuracy rate. In this study, WCE endoscopy images obtained from KID Atlas Dataset 2 were used and a three-stage artificial intelligence-supported diagnostic process was developed for the detection of the inflammatory anomaly, vascular anomaly, polypoid anomaly and normal image categories in the gastrointestinal tract. For the first stage, critical points on the images were clarified using 5 different approaches. These improved images were classified with a region proposal-based object recognition algorithm and performance comparison was made according to the approaches used. In the second stage, the data augmentation technique was applied to the improved images that showed maximum performance in the first stage. Thus, a dataset with a balanced and sufficient number of images was created. In the third stage, this current dataset was classified with five different object recognition algorithms. However, the individual success of each algorithm is different. For this reason, the ensemble learning approach was used to obtain stable outputs for each category and to create a balanced detection process among the categories. Finally, a balanced and stable estimation function was provided between categories with this hybrid structure.

Keywords

• Gastrointestinal anomalies
• Wireless capsule endoscopy
• Deep learning
• Ensemble learning

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