Alsulami Najla, Althobaiti Hassan, Alafif Tarik
Department of Computer Science in Jamoum, Umm Al-Qura University, Makkah 25371, Saudi Arabia.
Diagnostics (Basel). 2024 Jul 19;14(14):1566. doi: 10.3390/diagnostics14141566.
Pneumonia ranks among the most prevalent lung diseases and poses a significant concern since it is one of the diseases that may lead to death around the world. Diagnosing pneumonia necessitates a chest X-ray and substantial expertise to ensure accurate assessments. Despite the critical role of lateral X-rays in providing additional diagnostic information alongside frontal X-rays, they have not been widely used. Obtaining X-rays from multiple perspectives is crucial, significantly improving the precision of disease diagnosis. In this paper, we propose a multi-view multi-feature fusion model (MV-MFF) that integrates latent representations from a variational autoencoder and a β-variational autoencoder. Our model aims to classify pneumonia presence using multi-view X-rays. Experimental results demonstrate that the MV-MFF model achieves an accuracy of 80.4% and an area under the curve of 0.775, outperforming current state-of-the-art methods. These findings underscore the efficacy of our approach in improving pneumonia diagnosis through multi-view X-ray analysis.
肺炎是最常见的肺部疾病之一，也是全球范围内可能导致死亡的疾病之一，因此备受关注。诊断肺炎需要进行胸部X光检查，并需要大量专业知识以确保准确评估。尽管侧位X光在与正位X光一起提供额外诊断信息方面发挥着关键作用，但尚未得到广泛应用。从多个角度获取X光至关重要，可显著提高疾病诊断的准确性。在本文中，我们提出了一种多视图多特征融合模型（MV-MFF），该模型整合了变分自编码器和β-变分自编码器的潜在表示。我们的模型旨在使用多视图X光对肺炎的存在进行分类。实验结果表明，MV-MFF模型的准确率达到80.4%，曲线下面积为0.775，优于当前的先进方法。这些发现强调了我们的方法通过多视图X光分析改善肺炎诊断的有效性。
