CNN-Mamba Interaction Fusion Network for Diagnosis Retinopathy of Prematurity

Abstract

Retinopathy of prematurity (ROP) manifests clinically through abnormal development of the retinal capillary, ischemia, proliferative retinopathy, and retinal detachment, making it one of the leading causes of childhood blindness. Diagnosis of ROP from images of the full retinal fundus is challenging due to the presence of small peripheral lesions and underdeveloped ocular structures in premature infants. Current methods for fundus image analysis, based on Convolutional neural networks (CNNs) and transformers, often struggle to capture long-range dependencies or are limited by the complex computational demands. To address these challenges, we propose a CNN-Mamba interaction fusion network, CMIFNet, for diagnosing ROP. This network consists of a CNN-based branch for local feature extraction and a bidirectional state space model (BSSM) based branch for global information integration. We developed an interactive attention fusion module (IAFM) to enhance feature interaction and facilitate attention-based integration between the two branches. Our approach demonstrates promising recognition performance on a clinical ROP dataset.