Original scientific paper
https://doi.org/10.1080/00051144.2024.2352313
A novel approach to macular edema detection: DeepLabv3+ segmentation and VGG with vision transformer classification
C. Kotteeswari
; Department of Computer Science and Engineering, Velalar College of Engineering and Technology, Erode, India
*
V. Chandrasekaran
; Department of Electronics and Communication Engineering, VelalarCollege of Engineering and Technology, Erode, India
S. Anitha
; Department of Information Technology, Kongu Engineering College, Erode, India
* Corresponding author.
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
Hrčak ID:
326271
URI
Publication date:
13.5.2024.
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