  Glaucoma is the second driving reason for partial or complete blindness among
all the visual deficiencies which mainly occurs because of excessive pressure
in the eye due to anxiety or depression which damages the optic nerve and
creates complications in vision. Traditional glaucoma screening is a
time-consuming process that necessitates the medical professionals' constant
attention, and even so time to time due to the time constrains and pressure
they fail to classify correctly that leads to wrong treatment. Numerous efforts
have been made to automate the entire glaucoma classification procedure
however, these existing models in general have a black box characteristics that
prevents users from understanding the key reasons behind the prediction and
thus medical practitioners generally can not rely on these system. In this
article after comparing with various pre-trained models, we propose a transfer
learning model that is able to classify Glaucoma with 94.71\% accuracy. In
addition, we have utilized Local Interpretable Model-Agnostic
Explanations(LIME) that introduces explainability in our system. This
improvement enables medical professionals obtain important and comprehensive
information that aid them in making judgments. It also lessen the opacity and
fragility of the traditional deep learning models.
