An Attention Infused Deep Learning System with Grad-CAM Visualization for Early Screening of Glaucoma
arXiv:2505.17808v13 citationsh-index: 2
Originality Synthesis-oriented
AI Analysis
This work addresses early detection of glaucoma for medical diagnosis, but appears incremental as it builds on existing deep learning methods.
The paper tackled early glaucoma screening by combining a convolutional neural network with a Vision Transformer using a Cross Attention module, achieving results on the ACRIMA and Drishti datasets.
This research work reveals the eye opening wisdom of the hybrid labyrinthine deep learning models synergy born out of combining a trailblazing convolutional neural network with a disruptive Vision Transformer, both intertwined together with a radical Cross Attention module. Here, two high yielding datasets for artificial intelligence models in detecting glaucoma, namely ACRIMA and Drishti, are utilized.