Multi-Level Residual Feature Fusion Network for Thoracic Disease Classification in Chest X-Ray Images

Abstract

Motivation: Automated identification of thoracic diseases from chest X-ray images (CXR) is a significant area in computer-aided diagnosis. However, most existing methods have limited ability to extract multi-scale features and accurately capture the spatial location of lesions when dealing with thoracic diseases that exhibit concurrency and large variations in lesion size. Method: Based on the above problems, we propose a multi-level residual feature fusion network (MLRFNet) for classifying thoracic diseases. Our approach can quickly capture receptive field information across different lesion sizes and enhance disease-specific features within the spatial domain on feature maps. The MLRFNet comprises two main components: a feature extractor that learns multi-scale semantic information from chest X-ray images and a multi-level residual feature classifier (MRFC) that refines disease-specific pathological features at spatial locations to reduce interference from irrelevant regions. Additionally, the ECA attention modules connect both components to enable flexible channel-wise focus on critical pathological information. Results: We evaluated the performance of MLRFNet through a series of experiments on two datasets: ChestX-Ray14 and CheXpert. Our results show that MLRFNet achieves an average AUC of 0.853 on the ChestX-Ray14 dataset and 0.904 on the CheXpert dataset. Conclusion: The results of experiments demonstrate that our proposed method works better than the current state-of-the-art baselines. Future work will focus on investigating the interdependencies among labels for thoracic diseases and techniques for model compression.INDEX TERMS Chest X-ray image classification, convolutional neural network, attention mechanism, residual feature vector, medical image processing.

Privacy-Preserving Convolutional Bi-LSTM Network for Robust Analysis of Encrypted Time-Series Medical Images

“…The CheXpert (see Figure 1) dataset [46] is used for our investigations; it is a huge dataset with 224,316 chest X-rays from 65,240 individuals. (a) atelectasis, (b) cardiomegaly, (c) consolidation, (d) edema, and (e) pleural effusion are the five kinds that react to various thoracic diseases.…”

Patch-Level Feature Selection for Thoracic Disease Classification by Chest X-ray Images Using Information Bottleneck

“…In addition to the attention mechanisms, advantage techniques [15,16] were used to enhance accuracy. Typically, an advanced classifier comprises three main components: the backbone, neck, and head.…”

“…This mechanism generates an importance matrix to specify the significance of each element of the input feature, eliminating the need to approximate the posterior distribution for these critical features. Recently, MLRFNet [16] has greatly improved in CXR image-based diagnosis. This method utilizes Res2Net [17] as the backbone, the ECA module [18] as the neck, and the CSRA module [19] as the head.…”

“…In addition, the innovative design enables seamless integration of the P-VIB module into various CNN-based networks at any position. Consequently, we incorporate it into MLRFNet [16] to enhance accuracy. With the help of the proposed module, our method can work better in various scenarios.…”

“…• We made a connection between VIB and MLRFNet and proposed a patch-level feature selection to enhance disease diagnosis models based on MLRFNet [16] architecture.…”