MBHFuse: A multi-branch heterogeneous global and local infrared and visible image fusion with differential convolutional amplification features

Y Sun, M Dong, M Yu, L Zhu - Optics & Laser Technology, 2025 - Elsevier
Fusing infrared and visible imagery aims to harness their complementary spectral data,
enhancing output image quality, sharpness, and content. However, convolutional neural …

[HTML][HTML] Accurately assessing congenital heart disease using artificial intelligence

K Khan, F Ullah, I Syed, H Ali - PeerJ Computer Science, 2024 - peerj.com
Congenital heart disease (CHD) remains a significant global health challenge, particularly
contributing to newborn mortality, with the highest rates observed in middle-and low-income …

Development and External Validation of an Artificial Intelligence-Based Method for Scalable Chest Radiograph Diagnosis: A Multi-Country Cross-Sectional Study

Z Liu, J Xu, C Yin, G Han, Y Che, G Fan, X Li, L **e… - Research, 2024 - spj.science.org
Problem: Chest radiography is a crucial tool for diagnosing thoracic disorders, but
interpretation errors and a lack of qualified practitioners can cause delays in treatment. Aim …

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights

K Khan, F Ullah, I Syed, I Ullah - arxiv preprint arxiv:2501.04493, 2025 - arxiv.org
Congenital heart disease is among the most common fetal abnormalities and birth defects.
Despite identifying numerous risk factors influencing its onset, a comprehensive …

Explainable Machine Learning Model for Identifying Key Risk Factors in Congenital Heart Disease Prediction Using Questionnaire Data: A Retrospective Case …

Y Wang, D Li, T Li, J Chen, J Ruan, Q Shu… - Available at SSRN … - papers.ssrn.com
Background: Current neonatal congenital heart disease (CHD) screening strategy faces
significant challenges in low-income or underdeveloped regions due to a shortage of …