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<p>A digestive disease is a condition that affects the gastrointestinal (GI) tract, ranging from mild to severe. Common issues include heartburn, cancer, irritable bowel syndrome, and lactose intolerance. Additionally, other intestinal concerns such as bleeding, polyps, celiac disease, Crohn’s disease, ulcers, diverticulitis, small bowel syndrome, and intestinal ischemia represent further digestive illnesses. Wireless capsule endoscopy (WCE) is a device designed for endoscopic exploration that employs a compact camera to capture images of the GI tract as it passes through. WCE significantly aids in the identification of GI tract disorders. However, due to the capsule’s rapid transit and the potential for blurry, ambiguous images, abnormalities in certain regions of the intestine may be overlooked. Moreover, frames can become hazy if obstructed by food or stool waste. In this research, we propose a method for detecting abnormalities in WCE images to address these limitations. To achieve high classification performance through the extraction of both high-level and low-level features, we employ a deep CNN that integrates the DenseNet121 model with a residual block. Experimental studies demonstrate that our proposed method outperforms state-of-the-art techniques on two publicly available WCE datasets, one focused on bleeding and the other on various abnormalities. The accuracy of the proposed architecture is 96.96% for the Kvasir-Capsule Dataset and 99.50% for the MICCAI 2017 dataset.</p>

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Combined deep convolutional neural networks for abnormality classification in wireless capsule endoscopy images

  • 期刊论文

  • 发表日期：

    2025年3月26日

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  • Anass Garbaz,
  • Samira Lafraxo,
  • Said Charfi,
  • Mohamed El Ansari,
  • Lahcen Koutti,
  • Mouna Salihoun

摘要

A digestive disease is a condition that affects the gastrointestinal (GI) tract, ranging from mild to severe. Common issues include heartburn, cancer, irritable bowel syndrome, and lactose intolerance. Additionally, other intestinal concerns such as bleeding, polyps, celiac disease, Crohn’s disease, ulcers, diverticulitis, small bowel syndrome, and intestinal ischemia represent further digestive illnesses. Wireless capsule endoscopy (WCE) is a device designed for endoscopic exploration that employs a compact camera to capture images of the GI tract as it passes through. WCE significantly aids in the identification of GI tract disorders. However, due to the capsule’s rapid transit and the potential for blurry, ambiguous images, abnormalities in certain regions of the intestine may be overlooked. Moreover, frames can become hazy if obstructed by food or stool waste. In this research, we propose a method for detecting abnormalities in WCE images to address these limitations. To achieve high classification performance through the extraction of both high-level and low-level features, we employ a deep CNN that integrates the DenseNet121 model with a residual block. Experimental studies demonstrate that our proposed method outperforms state-of-the-art techniques on two publicly available WCE datasets, one focused on bleeding and the other on various abnormalities. The accuracy of the proposed architecture is 96.96% for the Kvasir-Capsule Dataset and 99.50% for the MICCAI 2017 dataset.