利用深度学习对多标签胸部 X 光片分类进行系统回顾

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Uswatun Hasanah, Jenq-Shiou Leu, Cries Avian, Ihsanul Azmi, Setya Widyawan Prakosa
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引用次数: 0

摘要

胸部 X 光扫描是识别胸部疾病最常用的诊断工具之一。然而,从 X 光图像中识别疾病需要经验丰富的技术人员,而且经常被认为是一个耗时的过程,解读的程度也不尽相同。在特殊情况下,通过图像识别疾病对人类观察者来说是一项挑战。深度学习的最新进展为使用这种技术诊断疾病提供了新的可能性。然而,进一步的实施需要事先了解策略和适当的架构设计。揭示这些信息将有助于更快地实施,并解决特定设计所产生的潜在问题,特别是在多标签分类方面,这与单标签任务相比具有挑战性。本研究对文献中发表的所有方法进行了系统回顾,这将有助于研究人员开发出更好的全胸疾病检测方法。本研究的重点是深度学习方法、可公开访问的数据集、超参数以及不同研究人员在对多标签胸部 X 光图像进行分类时采用的性能指标。研究结果全面概述了当前的技术水平,突出强调了所研究方法的重要实用性。研究结果突出了深度学习在多标签胸部疾病识别中的潜在优势和有益用途。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A systematic review of multilabel chest X-ray classification using deep learning

A systematic review of multilabel chest X-ray classification using deep learning

Chest X-ray scans are one of the most often used diagnostic tools for identifying chest diseases. However, identifying diseases in X-ray images needs experienced technicians and is frequently noted as a time-consuming process with varying levels of interpretation. In particular circumstances, disease identification through images is a challenge for human observers. Recent advances in deep learning have opened up new possibilities for using this technique to diagnose diseases. However, further implementation requires prior knowledge of strategy and appropriate architecture design. Revealing this information, will enable faster implementation and encounter potential issues produced by specific designs, especially in multilabel classification, which is challenging compared to single-label tasks. This systematic review of all the approaches published in the literature will assist researchers in developing improved methods of whole chest disease detection. The study focuses on the deep learning methods, publically accessible datasets, hyperparameters, and performance metrics employed by various researchers in classifying multilabel chest X-ray images. The findings of this study provide a complete overview of the current state of the art, highlighting significant practical aspects of the approaches studied. Distinctive results highlighting the potential enhancements and beneficial uses of deep learning in multilabel chest disease identification are presented.

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来源期刊
Multimedia Tools and Applications
Multimedia Tools and Applications 工程技术-工程:电子与电气
CiteScore
7.20
自引率
16.70%
发文量
2439
审稿时长
9.2 months
期刊介绍: Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools as well as case studies of multimedia applications. It also features experimental and survey articles. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed. Specific areas of interest include: - Multimedia Tools: - Multimedia Applications: - Prototype multimedia systems and platforms
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