Siddiqi Raheel, Javaid Sameena
Computer Science Department, Karachi Campus, Bahria University, Karachi 73500, Pakistan.
J Imaging. 2024 Jul 23;10(8):176. doi: 10.3390/jimaging10080176.
This paper addresses the significant problem of identifying the relevant background and contextual literature related to deep learning (DL) as an evolving technology in order to provide a comprehensive analysis of the application of DL to the specific problem of pneumonia detection via chest X-ray (CXR) imaging, which is the most common and cost-effective imaging technique available worldwide for pneumonia diagnosis. This paper in particular addresses the key period associated with COVID-19, 2020-2023, to explain, analyze, and systematically evaluate the limitations of approaches and determine their relative levels of effectiveness. The context in which DL is applied as both an aid to and an automated substitute for existing expert radiography professionals, who often have limited availability, is elaborated in detail. The rationale for the undertaken research is provided, along with a justification of the resources adopted and their relevance. This explanatory text and the subsequent analyses are intended to provide sufficient detail of the problem being addressed, existing solutions, and the limitations of these, ranging in detail from the specific to the more general. Indeed, our analysis and evaluation agree with the generally held view that the use of transformers, specifically, vision transformers (ViTs), is the most promising technique for obtaining further effective results in the area of pneumonia detection using CXR images. However, ViTs require extensive further research to address several limitations, specifically the following: biased CXR datasets, data and code availability, the ease with which a model can be explained, systematic methods of accurate model comparison, the notion of class imbalance in CXR datasets, and the possibility of adversarial attacks, the latter of which remains an area of fundamental research.
本文探讨了一个重要问题，即识别与深度学习（DL）这一不断发展的技术相关的背景和上下文文献，以便全面分析DL在通过胸部X光（CXR）成像进行肺炎检测这一特定问题上的应用。胸部X光成像是全球范围内用于肺炎诊断的最常见且最具成本效益的成像技术。本文特别关注与2020 - 2023年新冠疫情相关的关键时期，以解释、分析并系统评估方法的局限性，并确定它们的相对有效性水平。详细阐述了将DL既用作现有专家放射科医生（其可用性往往有限）的辅助工具又用作自动替代工具的应用背景。阐述了开展此项研究的基本原理，以及所采用资源及其相关性的理由。这段解释性文本及后续分析旨在提供关于所解决问题、现有解决方案及其局限性的足够详细信息，范围从具体到一般。实际上，我们的分析和评估与普遍观点一致，即使用Transformer，特别是视觉Transformer（ViT），是在使用CXR图像进行肺炎检测领域获得进一步有效结果的最有前途的技术。然而，ViT需要进一步广泛研究以解决几个局限性，具体如下：有偏差的CXR数据集、数据和代码的可用性、模型可解释的难易程度、准确模型比较的系统方法、CXR数据集中类不平衡的概念以及对抗攻击的可能性，其中对抗攻击仍是一个基础研究领域。
