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Dergiler / NATURENGS / 2024 / Volume: 5 Issue: 2

Automatic Detection of Gastrointestinal Diseases Using Wireless Capsule Endoscopy Images

Harun Bingöl(MALATYA TURGUT ÖZAL ÜNİVERSİTESİ)0000-0001-5071-4616
Muhammed Yıldırım(MALATYA TURGUT ÖZAL ÜNİVERSİTESİ)0000-0003-1866-4721
Sayfa
—
Yayın Tarihi
2024-12-30
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

It was seen that the proposed hybrid model achieved an accuracy value of 86.3%.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Gastrointestinal (GI) diseases are various disorders related to the digestive system.
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Temel Bulgular & Veri:As a result of the experiments, 5 different deep learning architectures were used.
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Sonuç & Katkı:It was seen that the proposed hybrid model achieved an accuracy value of 86.3%.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Gastrointestinal bleeding · Artificial intelligence · Deep learning

Abstract

Gastrointestinal (GI) diseases are various disorders related to the digestive system. This system includes the esophagus, stomach, small and large intestines, liver, gallbladder and pancreas, starting from the mouth. Early diagnosis is very important in the treatment of the disease. The earlier the disease is diagnosed, the higher the chance of the patient being treated. In recent years, it is known that artificial intelligence techniques have been widely used in disease diagnosis and classification. Among the artificial intelligence techniques, deep learning methods that produce very successful results in image classification are frequently used. This success of deep learning methods has been tried to be used in the classification of GI diseases. Within the scope of this study, it was tried to detect bleeding GI or lesions from publicly available wireless capsule endoscopy (WCE) images. As a result of the experiments, 5 different deep learning architectures were used. Features were extracted from the two architectures that showed the highest accuracy and were combined. Neighborhood Component Analysis (NCA) dimension reduction method was applied to the obtained feature map and a hybrid model was obtained. It was seen that the proposed hybrid model achieved an accuracy value of 86.3%.

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Bu Makaleye Atıf Yap

Bingöl, H., Yıldırım, M. (2024). Automatic Detection of Gastrointestinal Diseases Using Wireless Capsule Endoscopy Images. NATURENGS, Volume: 5 Issue: 2. https://doi.org/10.46572/naturengs.1567342

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Kaynakça (15)

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