DERİN ÖĞRENME ALGORİTMASI KULLANILARAK ENDOSKOPİ GÖRÜNTÜLERİNDE POLİPLERİN ARAŞTIRILMASI

Year 2022, Volume: 30 Issue: 3, 441 - 453, 21.12.2022

Emine Cengiz , Faik Yaylak , Eyyüp Gülbandılar

https://doi.org/10.31796/ogummf.1122707

Abstract

Makine öğrenimindeki, özellikle derin öğrenmeyle ilgili son gelişmeler, tıbbi görüntülerdeki nesneleri tanımaya ve sınıflandırmaya yardımcı olur. Bu çalışmada endoskopi görüntüleri incelenmiş, sağlıklı ve polipli hücrelerini sınıflandırılması için derin öğrenme yöntemi kullanılmıştır. Önerilen sistem için Kütahya Evliya Çelebi Eğitim ve Araştırma Hastanesi Genel Cerrahi Anabilim Dalı Endoskopi Ünitesi arşivlerinden bir veri tabanı oluşturulmuştur. Veri tabanı 54 arşiv kaydından; 93 polip ve 216 normal görüntü içermektedir. Görüntü çoğaltma için her görüntü kendi ekseni etrafında 90 derece döndürülerek toplam 1236 görüntü elde edilmiştir. Elde edilen bu verilerden rastgele seçilen verilerin 2/3'ü modelin eğitimi için kullanılırken, kalan veriler test için ayrılmıştır. Performans sonuçlarının değişkenliğini azaltmak için K-kat Çapraz Doğrulama yöntemi kullanıldı. Bu çalışmada, derin öğrenmede en iyi sınıflandırma modelini bulmak için farklı aktivasyon ve optimizasyon fonksiyonları kullanılarak 48 farklı model oluşturulmuştur. Deneysel sonuçlara göre, modellerin doğruluğunun seçilen parametrelere bağlı olduğu; %91 doğruluk oranı ile en iyi model gizli katmandaki 64 nöron, ReLU aktivasyon fonksiyonu ve RmsProp optimizasyon yöntemi ile elde edilirken, en kötü model %76 doğruluk oranı ile gizli katmandaki 32 nöron, Tanh aktivasyonu, PmsProp optimizasyon yöntemi ile elde edilmiştir. Buna göre, derin öğrenme modellerinin tasarımı sırasında farklı aktivasyon ve optimizasyon yöntemleri kullanılarak polip görüntülerinin sınıflandırma performansı optimize edilebilir.

Keywords

Derin öğrenme, Aktivasyon fonksiyonu, Optimizasyon methodu, Polip, Endoskopi

INVESTIGATION OF POLYPS IN ENDOSCOPY IMAGES BY USING DEEP LEARNING ALGORITHM

Abstract

Recent advances in machine learning, particularly with regard to deep learning, help to recognize and classify objects in medical images. In this study, endoscopy images were examined and deep learning method was used to classify healthy and polyp cells. For the proposed system, a database was created from the archives of General Surgery Department Endoscopy Unit in Kutahya Evliya Celebi Training and Research Hospital. The database contains 93 polyps and 216 normal images from 54 archive records. For image multiplexing, a total of 1236 images were obtained by rotating each image 90 degrees around its axis. While 2/3 of the randomly selected data from this obtained data was used for training the model, the rest of the data was reserved for testing. K-fold Cross Validation method was used to reduce the variability of performance results. In this study, 48 different models were created by using different activation and optimization functions to find the best classification model in deep learning. According to the experimental results, it was observed that accuracy of the models depends on the selected parameters; the best model with the accuracy rate of 91% was obtained with 64 neurons in the hidden layer, ReLU activation function and RmsProp optimization method whereas the worst model with the accuracy rate of 76% was obtained with 32 neurons in the hidden layer, Tanh activation and PmsProp optimization functions. Accordingly, classification performance of polyp images can be optimized by utilizing different activation and optimization methods during the design of deep learning models.

Keywords

Deep learning, Activation function, Optimization method, Polyp, Endoscopy

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