Optometrists nowadays use optical biopsy to get cross sectional images of the retina infected by
pathologies. This is also known as Optical Coherence Tomography (OCT). It is important to
identify the retinal diseases at an early stage to prevent damage to the vision. There is a lot of
research to be done to find a suitable method which can automatically detect retinal diseases.
Therefore, we propose this research for automatic detection of retinal diseases by using a novel
method of hyperparameter tuning instead of manually detecting the parameters of our
Convolutional Neural Network (CNN). The Model is tested on metrics such as F1-score,
precision, specificity, sensitivity, loss graph and accuracy. We also compare it with pretrained
state-of-the-art model of Inception V3 and result shows that hyperparameter-tuned CNN gets
better results. Being reliable, this proposed model can be used by optometrists to detect retinal
disorders at an early stage.