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Stomach Deformities Recognition Using Rank-Based Deep Features Selection

  • Image & Signal Processing
  • Published:
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Abstract

Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient’s deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complete process, more than 57,000 frames are captured and doctors need to examine a complete video frame by frame which is a tedious task even for an experienced gastrologist. In this article, a novel computerized automated method is proposed for the classification of abdominal infections of gastrointestinal track from WCE images. Three core steps of the suggested system belong to the category of segmentation, deep features extraction and fusion followed by robust features selection. The ulcer abnormalities from WCE videos are initially extracted through a proposed color features based low level and high-level saliency (CFbLHS) estimation method. Later, DenseNet CNN model is utilized and through transfer learning (TL) features are computed prior to feature optimization using Kapur’s entropy. A parallel fusion methodology is opted for the selection of maximum feature value (PMFV). For feature selection, Tsallis entropy is calculated later sorted into descending order. Finally, top 50% high ranked features are selected for classification using multilayered feedforward neural network classifier for recognition. Simulation is performed on collected WCE dataset and achieved maximum accuracy of 99.5% in 21.15 s.

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Authors and Affiliations

  1. Department of CS&E, HITEC University, Museum Road, Taxila, Pakistan

    Muhammad Attique Khan

  2. Department of CS, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan

    Ramesh Sunder Nayak

  3. Department of E&CE, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan

    Muhammad Sharif & Mussarat Yasmin

  4. Information Science, Canara Engineering College, Mangaluru, Karnataka, India

    Tallha Akram

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  1. Muhammad Attique Khan
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  2. Muhammad Sharif
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Correspondence to Muhammad Sharif.

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Khan, M.A., Sharif, M., Akram, T. et al. Stomach Deformities Recognition Using Rank-Based Deep Features Selection. J Med Syst 43, 329 (2019). https://doi.org/10.1007/s10916-019-1466-3

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