用注意力机制提升肺部X光片疾病识别准确率
AttCDCNet: Attention-enhanced Chest Disease Classification using X-Ray Images
- 在DenseNet121基础上加注意力模块，聚焦病灶区域
- 在新冠影像数据集上达94.94%准确率，优于原模型
- 轻量化设计适合临床部署，对小样本疾病有效
胸部X光片在肺炎、肺部阴影和新冠等疾病的诊断中已被证明有效。然而，传统医疗方法依赖人工判读，易受主观偏见影响，导致延误和误诊。为此，研究者提出基于深度学习的诊断技术，其中卷积神经网络（CNN）表现突出。本文提出一种新型检测模型AttCDCNet，通过在DenseNet121基础上引入注意力块，使模型更关注关键病灶区域；采用焦点损失函数缓解数据不平衡问题；使用深度可分离卷积减少参数量，提升模型轻量化程度。大量实验表明，该模型性能显著优于原始DenseNet121。在COVID-19 Radiography Dataset上，模型准确率、精确率和召回率分别达到94.94%、95.14%和94.53%。
原文摘要 · Abstract (English)
Chest X-rays (X-ray images) have been proven to be effective for the diagnosis of chest diseases, including Pneumonia, Lung Opacity, and COVID-19. However, relying on traditional medical methods for diagnosis from X-ray images is prone to delays and inaccuracies because the medical personnel who evaluate the X-ray images may have preconceived biases. For this reason, researchers have proposed the use of deep learning-based techniques to facilitate the diagnosis process. The preeminent method is the use of sophisticated Convolutional Neural Networks (CNNs). In this paper, we propose a novel detection model named \textbf{AttCDCNet} for the task of X-ray image diagnosis, enhancing the popular DenseNet121 model by adding an attention block to help the model focus on the most relevant regions, using focal loss as a loss function to overcome the imbalance of the dataset problem, and utilizing depth-wise convolution to reduce the parameters to make the model lighter. Through extensive experimental evaluations, the proposed model demonstrates exceptional performance, showing better results than the original DenseNet121. The proposed model achieved an accuracy, precision and recall of 94.94%, 95.14% and 94.53%, respectively, on the COVID-19 Radiography Dataset.
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