Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology, Center for Advanced Material Diagnostic Technology, and College of Engineering Physics, Shenzhen Technology University, Lantian Road, Shenzhen, Guangdong, 518118, China.
BMC Med Imaging. 2024 Jan 2;24(1):6. doi: 10.1186/s12880-023-01177-1.
In this paper, we propose an attention-enhanced architecture for improved pneumonia detection in chest X-ray images. A unique attention mechanism is integrated with ResNet to highlight salient features crucial for pneumonia detection. Rigorous evaluation demonstrates that our attention mechanism significantly enhances pneumonia detection accuracy, achieving a satisfactory result of 96% accuracy. To address the issue of imbalanced training samples, we integrate an enhanced focal loss into our architecture. This approach assigns higher weights to minority classes during training, effectively mitigating data imbalance. Our model's performance significantly improves, surpassing that of traditional approaches such as the pretrained ResNet-50 model. Our attention-enhanced architecture thus presents a powerful solution for pneumonia detection in chest X-ray images, achieving an accuracy of 98%. By integrating enhanced focal loss, our approach effectively addresses imbalanced training sample. Comparative analysis underscores the positive impact of our model's spatial and channel attention modules. Overall, our study advances pneumonia detection in medical imaging and underscores the potential of attention-enhanced architectures for improved diagnostic accuracy and patient outcomes. Our findings offer valuable insights into image diagnosis and pneumonia prevention, contributing to future research in medical imaging and machine learning.
在本文中，我们提出了一种注意力增强架构，用于提高胸部 X 光图像中的肺炎检测能力。一个独特的注意力机制与 ResNet 集成，突出对肺炎检测至关重要的显著特征。严格的评估表明，我们的注意力机制显著提高了肺炎检测的准确性，达到了令人满意的 96%的准确率。为了解决训练样本不平衡的问题，我们在架构中集成了增强型焦点损失。这种方法在训练过程中为少数类分配更高的权重，有效地减轻了数据不平衡的问题。我们的模型的性能显著提高，超过了传统方法，如预训练的 ResNet-50 模型。因此，我们的注意力增强架构为胸部 X 光图像中的肺炎检测提供了一种强大的解决方案，达到了 98%的准确率。通过集成增强型焦点损失，我们的方法有效地解决了训练样本不平衡的问题。对比分析强调了我们模型的空间和通道注意力模块的积极影响。总的来说，我们的研究推进了医学成像中的肺炎检测，并强调了注意力增强架构在提高诊断准确性和患者预后方面的潜力。我们的研究结果为图像诊断和肺炎预防提供了有价值的见解，为医学成像和机器学习的未来研究做出了贡献。
