Classificação do estágio de glaucoma usando dados multimodais

Abstract

Glaucoma is the leading cause of irreversible blindness worldwide. Its early diagnosis is challenging due to the absence of symptoms in the initial stages, the need for multiple exams to be analysed by specialised professionals, and the general lack of awareness about the disease among the population. Although the visual loss caused by glaucoma is irreversible, its progression can be slowed if the disease is detected in its early stages. In this context, deep learning methods have demonstrated promising results in medical image processing tasks, including classification and segmentation, offering potential support for clinical diagnosis. In this work, we developed a method for glaucoma stage classification that combines fundus photographs and OCT volumes. The method employs a multimodal convolutional architecture and explores various fusion strategies, both at the feature map and prediction levels, aiming to integrate multimodal information effectively. Additionally, specific regions of interest were investigated — the optic nerve in fundus photographs and the retinal layers in OCT volumes — to improve data representation and enhance classification accuracy. The experiments demonstrated that multimodal models outperformed unimodal approaches, achieving a Kappa score of 0.88, which indicates a high level of agreement of the proposed method with specialist assessments. Moreover, the results showed that fundus photography has a greater influence than OCT volumes in the classification process. At the same time, the targeted capture of retinal layers proved to be a promising strategy for further improving accuracy. Overall, the proposed method demonstrated significant potential as a clinical decision support tool, contributing to the advancement of automated diagnostic systems and enabling earlier and more accurate glaucoma detection.