2026/07/27 by Yi Li, Dengao Li, Jumin Zhao +2
Computer Science · Engineering · Medicine · #COVID-19 diagnosis using AI #Machine Learning in Healthcare #Medical Imaging and Analysis
paper · doi:10.1002/ima.70408
openalex publication_date 2026/07/27 · openalex created_date 2026/07/29 · openalex updated_date 2026/07/30
ABSTRACT Heart failure (HF) is a life‐threatening condition that poses a major global health burden. Accurate and automated detection of HF‐related manifestations—namely cardiomegaly, effusion, and edema—in chest X‐rays (CXRs) is of considerable clinical value for timely treatment. However, CXR abnormalities vary markedly in spatial scale, and the strong interdependencies among disease categories further complicate recognition, often resulting in degraded classification performance. To address these challenges, we introduce a compact, fully supervised classification network termed CFC, which integrates three complementary modules: (1) Coordinate Attention (CA) embedded in the backbone to enhance long‐range dependency modeling while preserving precise positional information; (2) a top‐down multiscale Feature‐wise Layer Fusion (FLF) pathway to effectively integrate semantic and spatial cues; and (3) a Class‐Specific Residual Attention (CSRA) head that concentrates on the most discriminative regions for each pathology. The proposed architecture was extensively evaluated on the CheXpert dataset, the ChestX‐ray14 dataset, and a proprietary in‐house clinical dataset. Compared with state‐of‐the‐art methods, our model achieved mean AUC scores of 0.9085, 0.9068, and 0.9253, respectively. These findings demonstrate that the CFC framework offers an effective and computationally efficient solution for HF diagnosis across both public benchmarks and real‐world clinical environments.