Medical Image Retrieval Using Empirical Mode Decomposition with Deep Convolutional Neural Network

Content-based medical image retrieval (CBMIR) systems attempt to search medical image database to narrow the semantic gap in medical image analysis. The efficacy of high-level medical information representation using features is a major challenge in CBMIR systems. Features play a vital role in the a...

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Vydané v:BioMed research international Ročník 2020; číslo 2020; s. 1 - 12
Hlavní autori: Zhang, Shaomin, Zhou, Tao, Zhi, Lijia
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
John Wiley & Sons, Inc
Predmet:
Artificial neural networks
Classification
Datasets
Decomposition
Deep learning
Diagnostic imaging
Digital imaging
Empirical analysis
Error analysis
Health aspects
Image analysis
Image management
Image processing
Image retrieval
Magnetic resonance imaging
Medical imaging
Medical technology
Methods
Neural networks
Search process
Semantics
Subject specialists
China
ISSN:2314-6133, 2314-6141, 2314-6141
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Shrnutí:Content-based medical image retrieval (CBMIR) systems attempt to search medical image database to narrow the semantic gap in medical image analysis. The efficacy of high-level medical information representation using features is a major challenge in CBMIR systems. Features play a vital role in the accuracy and speed of the search process. In this paper, we propose a deep convolutional neural network- (CNN-) based framework to learn concise feature vector for medical image retrieval. The medical images are decomposed into five components using empirical mode decomposition (EMD). The deep CNN is trained in a supervised way with multicomponent input, and the learned features are used to retrieve medical images. The IRMA dataset, containing 11,000 X-ray images, 116 classes, is used to validate the proposed method. We achieve a total IRMA error of 43.21 and a mean average precision of 0.86 for retrieval task and IRMA error of 68.48 and F1 measure of 0.66 on classification task, which is the best result compared with existing literature for this dataset.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Academic Editor: Lin Gu
ISSN:2314-6133
2314-6141
2314-6141
DOI:10.1155/2020/6687733