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Erschienen in:
Know-GRRF: Domain-Knowledge Informed Biomarker Discovery with Random Forests Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

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

verfasst von : Joke A. Badejo, Emmanuel Adetiba, Adekunle Akinrinmade, Matthew B. Akanle

Erschienen in: Bioinformatics and Biomedical Engineering

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Detection of Irregular Thermoregulation in Hand Thermography by Fuzzy C-Means
Nächstes Kapitel Classification of Breast Cancer Histopathological Images Using KAZE Features
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Metadaten
Titel
Medical Image Classification with Hand-Designed or Machine-Designed Texture Descriptors: A Performance Evaluation
verfasst von
Joke A. Badejo
Emmanuel Adetiba
Adekunle Akinrinmade
Matthew B. Akanle
Copyright-Jahr
2018
Buch
Bioinformatics and Biomedical Engineering

Print ISBN: 978-3-319-78758-9

Electronic ISBN: 978-3-319-78759-6

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-78759-6_25

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