Multiple semantic X-ray medical image retrieval using efficient feature vector extracted by FPN.

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-01-01 DOI:10.3233/XST-240069

Lijia Zhi, Shaoyong Duan, Shaomin Zhang

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引用次数: 0

Abstract

Objective: Content-based medical image retrieval (CBMIR) has become an important part of computer-aided diagnostics (CAD) systems. The complex medical semantic information inherent in medical images is the most difficult part to improve the accuracy of image retrieval. Highly expressive feature vectors play a crucial role in the search process. In this paper, we propose an effective deep convolutional neural network (CNN) model to extract concise feature vectors for multiple semantic X-ray medical image retrieval.

Methods: We build a feature pyramid based CNN model with ResNet50V2 backbone to extract multi-level semantic information. And we use the well-known public multiple semantic annotated X-ray medical image data set IRMA to train and test the proposed model.

Results: Our method achieves an IRMA error of 32.2, which is the best score compared to the existing literature on this dataset.

Conclusions: The proposed CNN model can effectively extract multi-level semantic information from X-ray medical images. The concise feature vectors can improve the retrieval accuracy of multi-semantic and unevenly distributed X-ray medical images.

查看原文本刊更多论文

利用 FPN 提取的高效特征向量进行多语义 X 射线医学图像检索。

目的：基于内容的医学图像检索（CBMIR）已成为计算机辅助诊断（CAD）系统的重要组成部分。医学图像中固有的复杂医学语义信息是提高图像检索准确性的最大难点。高表现力的特征向量在检索过程中起着至关重要的作用。本文提出了一种有效的深度卷积神经网络（CNN）模型，以提取简洁的特征向量，用于多语义 X 射线医学图像检索：方法：我们以 ResNet50V2 为骨干建立了一个基于特征金字塔的 CNN 模型，以提取多层次语义信息。方法：我们以 ResNet50V2 为骨干建立了基于特征金字塔的 CNN 模型，提取多层次语义信息，并使用著名的公共多语义注释 X 射线医学图像数据集 IRMA 来训练和测试所提出的模型：结果：与现有文献相比，我们的方法在 IRMA 数据集上取得了 32.2 的最佳成绩：结论：所提出的 CNN 模型能有效地从 X 光医学图像中提取多层次语义信息。结论：所提出的 CNN 模型能有效地从 X 光医学图像中提取多层次语义信息，简洁的特征向量能提高多语义和分布不均的 X 光医学图像的检索精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.