ÖZNİTELİK ENTEGRASYONUNA DAYALI ESA MİMARİSİ KULLANILARAK ENDOSKOPİK GÖRÜNTÜLERİN SINIFLANDIRILMASI

Hüseyin Üzen; Hüseyin Fırat

doi:10.17780/ksujes.1362792

EN TR

CLASSIFICATION OF ENDOSCOPIC IMAGES USING CNN ARCHITECTURE BASED ON FEATURE INTEGRATION

Abstract

Recent developments in deep learning (DL) techniques show promising potential for automating the classification of gastrointestinal (GI) diseases using medical images. Timely and accurate diagnosis significantly impacts treatment effectiveness. This research introduces a new DL-based model for identifying GI diseases. This model performs classification by combining features extracted from intermediate layers of pretrained network architectures. In this model, named CNN based on feature integration, high and low-level features from pretrained network architectures are combined to obtain a final feature map for classifying endoscopic images. This feature map is then utilized for classification. Experimental analyses conducted using the Kvasirv2 dataset resulted in successful performance with the proposed model. Specifically, combining features from the intermediate layers of the DenseNet201 model resulted in accuracies, precision, recall, and F1 scores of 94.25%, 94.28%, 94.24%, and 94.24%, respectively. Comparative analyses against other CNN-based pretrained models and recent studies highlighted the superiority of the proposed model, elevating the accuracy to 94.25%. This underscores the potential of leveraging features from the intermediate layers of DenseNet201 for enhanced classification accuracy in detecting GI diseases from endoscopic images

Keywords

ÖZNİTELİK ENTEGRASYONUNA DAYALI ESA MİMARİSİ KULLANILARAK ENDOSKOPİK GÖRÜNTÜLERİN SINIFLANDIRILMASI

Öz

Derin öğrenme (DL) tekniklerindeki son gelişmeler, tıbbi görüntüler kullanılarak gastrointestinal (GI) hastalıkların sınıflandırılmasını otomatikleştirmek için umut verici bir potansiyel göstermektedir. Zamanında ve kesin teşhis, tedavi etkinliğini önemli ölçüde etkilemektedir. Bu araştırma, GI hastalıklarını tanımlamak için yeni bir DL tabanlı modeli tanıtmaktadır. Bu model, önceden eğitilmiş ağ mimarilerinin ara katmanlarından elde edilen öznitelikleri birleştirerek sınıflandırma işlemini gerçekleştirmektedir. Öznitelik entegrasyonuna dayalı evrişimsel sinir ağı (ESA) olarak adlandırılan bu modelde, endoskopik görüntüleri sınıflandırmak için önceden eğitilmiş ağ mimarilerinin yüksek ve düşük seviyeli öznitelikleri birleştirilerek nihai öznitelik haritası elde edilmektedir. Daha sonra bu öznitelik haritası sınıflandırma için kullanılmaktadır. Kvasirv2 veri seti kullanılarak yapılan deneysel analizler sonucunda, önerilen model ile başarılı bir performans elde edilmiştir. Özellikle, DenseNet201 modelinin ara katmanlarındaki özelliklerin birleştirilmesi, sırasıyla %94.25, %94.28, %94.24 ve %94.24 doğruluk, kesinlik, duyarlılık ve F1 puanı ile sonuçlanmıştır. Diğer ESA tabanlı önceden eğitilmiş modellerle ve son çalışmalarla yapılan karşılaştırmalı analizler, önerilen modelin üstünlüğünü ortaya koymuş ve doğruluğu %94.25'e yükseltmiştir. Bu, endoskopik görüntülerden GI hastalık tespitinde gelişmiş sınıflandırma doğruluğu için DenseNet201'in ara katmanlarındaki özelliklerden yararlanma potansiyelinin altını çizmektedir.

Anahtar Kelimeler

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Ayrıntılar

Birincil Dil

Türkçe

Konular

Derin Öğrenme

Bölüm

Araştırma Makalesi

Yazarlar

Hüseyin Üzen
0000-0002-0998-2130
Türkiye

Hüseyin Fırat ^*
0000-0002-1257-8518
Türkiye

Yayımlanma Tarihi

3 Mart 2024

Gönderilme Tarihi

19 Eylül 2023

Kabul Tarihi

29 Kasım 2023

Yayımlandığı Sayı

Yıl 1970 Cilt: 27 Sayı: 1

DOI

https://doi.org/10.17780/ksujes.1362792

IZ

https://izlik.org/JA77TG75EB

Kaynak Göster

RIS / Bibtex

APA

Üzen, H., & Fırat, H. (2024). ÖZNİTELİK ENTEGRASYONUNA DAYALI ESA MİMARİSİ KULLANILARAK ENDOSKOPİK GÖRÜNTÜLERİN SINIFLANDIRILMASI. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(1), 121-132. https://doi.org/10.17780/ksujes.1362792

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