Image detection of aortic dissection complications based on multi-scale feature fusion
© 2024 The Authors..
Background: Aortic dissection refers to the true and false two-lumen separation of the aortic wall, in which the blood in the aortic lumen enters the aortic mesomembrane from the tear of the aortic intima to separate the mesomembrane and expand along the long axis of the aorta.
Purpose: In view of the problems of individual differences, complex complications and many small targets in clinical aortic dissection detection, this paper proposes a convolution neural network MFF-FPN (Multi-scale Feature Fusion based Feature Pyramid Network) for the detection of aortic dissection complications.
Methods: The proposed model uses Resnet50 as the backbone for feature extraction and builds a pyramid structure to fuse low-level and high-level feature information. We add an attention mechanism to the backbone network, which can establish inter-dependencies between feature graph channels and enhance the representation quality of CNN.
Results: The proposed method has a mean average precision (MAP) of 99.40% in the task of multi object detection for aortic dissection and complications, which is higher than the accuracy of 96.3% on SSD model and 99.05% on YoloV7 model. It greatly improves the accuracy of small target detection such as cysts, making it more suitable for clinical focus detection.
Conclusions: The proposed deep learning model achieves feature reuse and focuses on local important information. By adding only a small number of model parameters, we are able to greatly improve the detection accuracy, which is effective in detecting small target lesions commonly found in clinical settings, and also performs well on other medical and natural datasets.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Heliyon - 10(2024), 6 vom: 30. März, Seite e27678 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Tan, Yun [VerfasserIn] |
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| Links: |
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| Themen: |
Convolution neural network |
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| Anmerkungen: |
Date Revised 28.03.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.heliyon.2024.e27678 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370226062 |
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| 245 | 1 | 0 | |a Image detection of aortic dissection complications based on multi-scale feature fusion |
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| 500 | |a Date Revised 28.03.2024 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2024 The Authors. | ||
| 520 | |a Background: Aortic dissection refers to the true and false two-lumen separation of the aortic wall, in which the blood in the aortic lumen enters the aortic mesomembrane from the tear of the aortic intima to separate the mesomembrane and expand along the long axis of the aorta | ||
| 520 | |a Purpose: In view of the problems of individual differences, complex complications and many small targets in clinical aortic dissection detection, this paper proposes a convolution neural network MFF-FPN (Multi-scale Feature Fusion based Feature Pyramid Network) for the detection of aortic dissection complications | ||
| 520 | |a Methods: The proposed model uses Resnet50 as the backbone for feature extraction and builds a pyramid structure to fuse low-level and high-level feature information. We add an attention mechanism to the backbone network, which can establish inter-dependencies between feature graph channels and enhance the representation quality of CNN | ||
| 520 | |a Results: The proposed method has a mean average precision (MAP) of 99.40% in the task of multi object detection for aortic dissection and complications, which is higher than the accuracy of 96.3% on SSD model and 99.05% on YoloV7 model. It greatly improves the accuracy of small target detection such as cysts, making it more suitable for clinical focus detection | ||
| 520 | |a Conclusions: The proposed deep learning model achieves feature reuse and focuses on local important information. By adding only a small number of model parameters, we are able to greatly improve the detection accuracy, which is effective in detecting small target lesions commonly found in clinical settings, and also performs well on other medical and natural datasets | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Convolution neural network | |
| 650 | 4 | |a Multiscale-feature | |
| 650 | 4 | |a Small target | |
| 650 | 4 | |a Target detection | |
| 700 | 1 | |a Wang, Zhenxu |e verfasserin |4 aut | |
| 700 | 1 | |a Tan, Ling |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Chunzhi |e verfasserin |4 aut | |
| 700 | 1 | |a Deng, Chao |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jingyu |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Qin, Jiaohua |e verfasserin |4 aut | |
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