Journal of Medical Systems

, 43:329 | Cite as

Stomach Deformities Recognition Using Rank-Based Deep Features Selection

  • Muhammad Attique Khan
  • Muhammad SharifEmail author
  • Tallha Akram
  • Mussarat Yasmin
  • Ramesh Sunder Nayak
Image & Signal Processing
Part of the following topical collections:
  1. Recent Advances in Deep Learning for Biomedical Signal Processing, Health Informatics and Computer Vision


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.


Colorectal cancer WCE Saliency estimation Deep features selection Features fusion 


Compliance with ethical standards

Conflict of interest

All authors have no conflict of interest and contribute equally in this work for results compilation and other technical support.

Ethical approval (for animals)

Not Applicable.

Ethical approval (for human)

The datasets which are used in this work are publically available such as PH2, ISBI 2016 and ISBI 2017.

Informed consent

Not Applicable.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Muhammad Attique Khan
    • 1
  • Muhammad Sharif
    • 3
    Email author
  • Tallha Akram
    • 4
  • Mussarat Yasmin
    • 3
  • Ramesh Sunder Nayak
    • 2
  1. 1.Department of CS&EHITEC UniversityTaxilaPakistan
  2. 2.Department of CSCOMSATS University IslamabadIslamabadPakistan
  3. 3.Department of E&CECOMSATS University IslamabadIslamabadPakistan
  4. 4.Information ScienceCanara Engineering CollegeMangaluruIndia

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