  In medical science, the use of computer science in disease detection and
diagnosis is gaining popularity. Previously, the detection of disease used to
take a significant amount of time and was less reliable. Machine learning (ML)
techniques employed in recent biomedical researches are making revolutionary
changes by gaining higher accuracy with more concise timing. At present, it is
even possible to automatically detect diseases from the scanned images with the
help of ML. In this research, we have taken such an attempt to detect retinal
diseases from optical coherence tomography (OCT) X-ray images. Here, we propose
a deep learning (DL) based approach in detecting retinal diseases from OCT
images which can identify three conditions of the retina. Four different models
used in this approach are compared with each other. On the test set, the
detection accuracy is 98.00\% for a vanilla convolutional neural network (CNN)
model, 99.07\% for Xception model, 97.00\% for ResNet50 model, and 99.17\% for
MobileNetV2 model. The MobileNetV2 model acquires the highest accuracy, and the
closest to the highest is the Xception model. The proposed approach has a
potential impact on creating a tool for automatically detecting retinal
diseases.
