Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (6)
  • Open Access


    Deep Convolutional Neural Networks for Accurate Classification of Gastrointestinal Tract Syndromes

    Zahid Farooq Khan1, Muhammad Ramzan1,*, Mudassar Raza1, Muhammad Attique Khan2,3, Khalid Iqbal4, Taerang Kim5, Jae-Hyuk Cha5

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1207-1225, 2024, DOI:10.32604/cmc.2023.045491

    Abstract Accurate detection and classification of artifacts within the gastrointestinal (GI) tract frames remain a significant challenge in medical image processing. Medical science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous diseases. Key to this is the development of robust algorithms for image classification and detection, crucial in designing sophisticated systems for diagnosis and treatment. This study makes a small contribution to endoscopic image classification. The proposed approach involves multiple operations, including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and Xception. Additionally, feature optimization utilizes the binary dragonfly algorithm… More >

  • Open Access


    Gastrointestinal Diseases Classification Using Deep Transfer Learning and Features Optimization

    Mousa Alhajlah1, Muhammad Nouman Noor2, Muhammad Nazir2, Awais Mahmood1,*, Imran Ashraf3, Tehmina Karamat4

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2227-2245, 2023, DOI:10.32604/cmc.2023.031890

    Abstract Gastrointestinal diseases like ulcers, polyps’, and bleeding are increasing rapidly in the world over the last decade. On average 0.7 million cases are reported worldwide every year. The main cause of gastrointestinal diseases is a Helicobacter Pylori (H. Pylori) bacterium that presents in more than 50% of people around the globe. Many researchers have proposed different methods for gastrointestinal disease using computer vision techniques. Few of them focused on the detection process and the rest of them performed classification. The major challenges that they faced are the similarity of infected and healthy regions that misleads the correct classification accuracy. In… More >

  • Open Access


    Ensembles of Deep Learning Framework for Stomach Abnormalities Classification

    Talha Saeed, Chu Kiong Loo*, Muhammad Shahreeza Safiruz Kassim

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4357-4372, 2022, DOI:10.32604/cmc.2022.019076


    Abnormalities of the gastrointestinal tract are widespread worldwide today. Generally, an effective way to diagnose these life-threatening diseases is based on endoscopy, which comprises a vast number of images. However, the main challenge in this area is that the process is time-consuming and fatiguing for a gastroenterologist to examine every image in the set. Thus, this led to the rise of studies on designing AI-based systems to assist physicians in the diagnosis. In several medical imaging tasks, deep learning methods, especially convolutional neural networks (CNNs), have contributed to the state-of-the-art outcomes, where the complicated nonlinear relation between target classes and… More >

  • Open Access


    Gastrointestinal Tract Infections Classification Using Deep Learning

    Muhammad Ramzan1, Mudassar Raza1, Muhammad Sharif1, Muhammad Attique Khan2, Yunyoung Nam3,*

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3239-3257, 2021, DOI:10.32604/cmc.2021.015920

    Abstract Automatic gastrointestinal (GI) tract disease recognition is an important application of biomedical image processing. Conventionally, microscopic analysis of pathological tissue is used to detect abnormal areas of the GI tract. The procedure is subjective and results in significant inter-/intra-observer variations in disease detection. Moreover, a huge frame rate in video endoscopy is an overhead for the pathological findings of gastroenterologists to observe every frame with a detailed examination. Consequently, there is a huge demand for a reliable computer-aided diagnostic system (CADx) for diagnosing GI tract diseases. In this work, a CADx was proposed for the diagnosis and classification of GI… More >

  • Open Access


    Segmentation and Classification of Stomach Abnormalities Using Deep Learning

    Javeria Naz1, Muhammad Attique Khan1, Majed Alhaisoni2, Oh-Young Song3,*, Usman Tariq4, Seifedine Kadry5

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 607-625, 2021, DOI:10.32604/cmc.2021.017101

    Abstract An automated system is proposed for the detection and classification of GI abnormalities. The proposed method operates under two pipeline procedures: (a) segmentation of the bleeding infection region and (b) classification of GI abnormalities by deep learning. The first bleeding region is segmented using a hybrid approach. The threshold is applied to each channel extracted from the original RGB image. Later, all channels are merged through mutual information and pixel-based techniques. As a result, the image is segmented. Texture and deep learning features are extracted in the proposed classification task. The transfer learning (TL) approach is used for the extraction… More >

  • Open Access


    Development of a Gastrointestinal Tract Microscale Cell Culture Analog to Predict Drug Transport

    Gretchen J. McAuliffe*, Jung Yun Chang, Raymond P. Glahn, Michael L. Shuler§

    Molecular & Cellular Biomechanics, Vol.5, No.2, pp. 119-132, 2008, DOI:10.3970/mcb.2008.005.119

    Abstract Microscale cell culture analogs (μCCAs) are used to study the metabolism and toxicity of a chemical or drug. These in vitro devices are physical replicas of physiologically based pharmacokinetic models that combine microfabrication and cell culture. The goal of this project is to add an independent GI tract μCCA to a multi-chamber chip μCCA representing the primary circulation. The GI tract μCCA consists of two chambers separated by a microporous membrane on which intestinal epithelial cells are cultured. Compounds of interest are pumped through the top chamber, allowing drug to be absorbed through the epithelial layer and circulated into the… More >

Displaying 1-10 on page 1 of 6. Per Page