Deep Learning-Based Automatic Detection of Central Serous Retinopathy using Optical Coherence Tomographic Images

Central Serous Retinopathy (CSR), also known as Central Serous Chorioretinopathy (CSC), occurs due to the clotting of fluids behind the retinal surface. The retina is composed of thin tissues that capture light and transform into visual recognition in the brain. This significant and critical organ may be damaged and causes vision loss and blindness for the individuals. Therefore, early-stage detection of the syndrome may cure complete loss of vision and, in some cases, may recover to its normal state. Hence, accurate and fast detection of CSR saves macula from severe damage and provides a basis for detecting other retinal pathologies. The Optical Coherence Tomographic (OCT) images have been used to detect CSR, but the design of a computationally efficient and accurate system remains a challenge. This research develops a framework for accurate and automatic CSR detection from OCT images using pre-trained deep convolutional neural networks. The preprocessing of OCT image enhances and filters the images for improving contrast and eliminate noise, respectively. Pre-trained network architectures have been employed, which are; AlexNet, ResNet-18, and GoogleNet for classification. The classification scheme followed by preprocessing enhances the foreground objects from OCT images. The performance of deep CNN has been compared through a statistical evaluation of parameters. The statistical parameters evaluation has shown 99.64% classification accuracy for AlexNet using Optical Coherence Tomography Image Database (OCTID). This shows the suitability of the proposed framework in clinical application to help doctors and clinicians diagnose retinal diseases.