Medical Image Classification with Hand-Designed or Machine-Designed Texture Descriptors: A Performance Evaluation

  • Joke A. BadejoEmail author
  • Emmanuel Adetiba
  • Adekunle Akinrinmade
  • Matthew B. Akanle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10814)


Accurate diagnosis and early detection of various disease conditions are key to improving living conditions in any community. The existing framework for medical image classification depends largely on advanced digital image processing and machine (deep) learning techniques for significant improvement. In this paper, the performance of traditional hand-designed texture descriptors within the image-based learning paradigm is evaluated in comparison with machine-designed descriptors (extracted from pre-trained Convolution Neural Networks). Performance is evaluated, with respect to speed, accuracy and storage requirements, based on four popular medical image datasets. The experiments reveal an increased accuracy with machine-designed descriptors in most cases, though at a higher computational cost. It is therefore necessary to consider other parameters for tradeoff depending on the application being considered.


Medical image classification Deep learning Convolution Neural Network Texture descriptors 



This research was sponsored under the Centre for Research Innovation and Development Research Grant of Covenant University. The authors who shared their MATLAB code and toolboxes for LBP, LTP, LPQ, CLBP, RICLBP and MatConvNet are appreciated.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Joke A. Badejo
    • 1
    Email author
  • Emmanuel Adetiba
    • 1
    • 2
    • 3
  • Adekunle Akinrinmade
    • 1
  • Matthew B. Akanle
    • 1
    • 2
  1. 1.Department of Electrical and Information EngineeringCovenant UniversityOtaNigeria
  2. 2.Center for Systems and Information Services (CSIS)Covenant UniversityOtaNigeria
  3. 3.HRA, Institute for Systems ScienceDurban University of TechnologyDurbanSouth Africa

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