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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| Vydáno v: | BioMed research international Ročník 2020; číslo 2020; s. 1 - 12 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Cairo, Egypt
Hindawi Publishing Corporation
2020
Hindawi John Wiley & Sons, Inc |
| Témata: | |
| ISSN: | 2314-6133, 2314-6141, 2314-6141 |
| On-line přístup: | Získat plný text |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Zhi, Lijia Zhou, Tao Zhang, Shaomin |
| AuthorAffiliation | School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China |
| AuthorAffiliation_xml | – name: School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China |
| Author_xml | – sequence: 1 fullname: Zhang, Shaomin – sequence: 2 fullname: Zhou, Tao – sequence: 3 fullname: Zhi, Lijia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33426062$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_3233_XST_240069 crossref_primary_10_1007_s11227_022_04535_y crossref_primary_10_1007_s11227_024_06350_z crossref_primary_10_1155_2022_7020804 crossref_primary_10_1155_2022_6256126 crossref_primary_10_1186_s12891_025_08807_5 |
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| ContentType | Journal Article |
| Copyright | Copyright © 2020 Shaomin Zhang et al. COPYRIGHT 2020 John Wiley & Sons, Inc. Copyright © 2020 Shaomin Zhang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2020 Shaomin Zhang et al. 2020 |
| Copyright_xml | – notice: Copyright © 2020 Shaomin Zhang et al. – notice: COPYRIGHT 2020 John Wiley & Sons, Inc. – notice: Copyright © 2020 Shaomin Zhang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 – notice: Copyright © 2020 Shaomin Zhang et al. 2020 |
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| Snippet | Content-based medical image retrieval (CBMIR) systems attempt to search medical image database to narrow the semantic gap in medical image analysis. The... Content‐based medical image retrieval (CBMIR) systems attempt to search medical image database to narrow the semantic gap in medical image analysis. The... |
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| SubjectTerms | 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 |
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| Title | Medical Image Retrieval Using Empirical Mode Decomposition with Deep Convolutional Neural Network |
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