A novel approach to macular edema detection: DeepLabv3+ segmentation and VGG with vision transformer classification

Abstract

The domain of deep learning has seen significant advancements, particularly in the context of detecting macular edema from images of the retina, in recent times. This study introduces an innovative model for identifying macular edema, employing two deep learning models: Deeplabv3 + and VGG with a vision transformer. The Deeplabv3 + model is used to segment the macula region in the retinal images. The segmented macula region is then fed into the VGG for feature extraction with a vision transformer model for detection. This approach leverages the strengths of both models in detecting accurately and efficiently. The Deeplabv3 + model can accurately segment the macula region, which is crucial for accurate detection. The VGG combined with a vision transformer model proves highly efficient in detecting even subtle changes in the macular region, signifying the existence of macular edema. The results of our experiments with the dataset show that the proposed method outperforms current cutting-edge techniques. With an outstanding precision rate of 99.53%, the suggested approach firmly solidifies its superiority. The results highlight the effectiveness of the proposed technique in precisely and effectively detecting pathological fluid accumulation in retina images. This ability can have a substantial influence on the early detection and management of eye disorders.

KEYWORDS:

• Macular edema
• DeepLabv3+
• deep learning
• VGG
• vision transformer
• segmentation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Human and animal rights

No violation of Human and Animal Rights is involved.

Data availability statement

Data sharing is applicable to this article as a publicly available dataset analyzed during the current study