SE-Fundusnet: A Channel Attention-Based Deep Learning Architecture for Papilledema Detection and Classification
Year: 2025
Type: article
Abstract: Papilledema is characterized by enlargement of the optic disc resulting from increased intracranial pressure, and represents a critical clinical finding that demands immediate medical attention. A new deep learning method for automatically identifying papilledema from fundus images is presented in this work. A Hybrid Centric convolutional neural network (HCCNN) architecture is introduced that incorporates squeeze-and-excite (SE) channel attention mechanisms and parallel feature extraction to effectively capture diagnostically relevant features. The model employs different kernel sizes across parallel processing pathways to simultaneously extract fine-grained details and broader contextual features. Experimental results demonstrate excellent performance, with an overall accuracy of 96.98%, sensitivity of 95.48% and specificity of 97.74%. Ablation studies confirm the effectiveness of the SE blocks, which improve detection accuracy by 4.3%, particularly for subtle presentations. This work represents a significant step toward developing reliable computer-aided diagnostic tools for papilledema, potentially enhancing early detection and monitoring of this vision-threatening condition.
DOI: 10.14419/kgcxv072
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4415357917