A transfer learning-based multimodal neural network combining metadata and multiple medical images for glaucoma type diagnosis.

ABSTRACT: Glaucoma is an acquired optic neuropathy, which can lead to irreversible vision loss. Deep learning(DL), especially convolutional neural networks(CNN), has achieved considerable success in the field of medical image recognition due to the availability of large-scale annotated datasets and CNNs. However, obtaining fully annotated datasets like ImageNet in the medical field is still a challenge. Meanwhile, single-modal approaches remain both unreliable and inaccurate due to the diversity of glaucoma disease types and the complexity of symptoms. In this paper, a new multimodal dataset for glaucoma is constructed and a new multimodal neural network for glaucoma diagnosis and classification (GMNNnet) is proposed aiming to address both of these issues. Specifically, the dataset includes the five

SUBMITTER: Li Y 

PROVIDER: S-EPMC10372152 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature