Abstract
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.
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Acknowledgements
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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Badejo, J.A., Adetiba, E., Akinrinmade, A., Akanle, M.B. (2018). Medical Image Classification with Hand-Designed or Machine-Designed Texture Descriptors: A Performance Evaluation. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2018. Lecture Notes in Computer Science(), vol 10814. Springer, Cham. https://doi.org/10.1007/978-3-319-78759-6_25
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DOI: https://doi.org/10.1007/978-3-319-78759-6_25
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