  In the world of medical diagnostics, the adoption of various deep learning
techniques is quite common as well as effective, and its statement is equally
true when it comes to implementing it into the retina Optical Coherence
Tomography (OCT) sector, but (i)These techniques have the black box
characteristics that prevent the medical professionals to completely trust the
results generated from them (ii)Lack of precision of these methods restricts
their implementation in clinical and complex cases (iii)The existing works and
models on the OCT classification are substantially large and complicated and
they require a considerable amount of memory and computational power, reducing
the quality of classifiers in real-time applications. To meet these problems,
in this paper a self-developed CNN model has been proposed which is
comparatively smaller and simpler along with the use of Lime that introduces
Explainable AI to the study and helps to increase the interpretability of the
model. This addition will be an asset to the medical experts for getting major
and detailed information and will help them in making final decisions and will
also reduce the opacity and vulnerability of the conventional deep learning
models.
