This work presents the diagnosis of glaucoma in the early stage using deep learning models. Glaucoma is an irreversible disease that can cause blindness. Manual diagnosis depends on human skills, and it isn’t easy to diagnose early. Glaucoma classification using deep learning is a challenging task. The concept of feature extraction using deep learning models has been utilized in the current study. An ensemble residual network, squeeze and excitation block, and Binary Long Short-Term Memory (BiLSTM) have been implemented for sequential feature extraction for faster and more accurate disease classification. A residual network is used for global feature extraction. The accuracy rate of the validation set was 92.3% for our model. The findings suggest that a cost-effective screening tool for early and cost-effective identification of glaucoma could be developed utilizing deep learning algorithms.