Glaucoma is characterized by increased intraocular pressure and damage to the optic nerve, which may result in irreversible blindness. The drastic effects of this disease can be avoided if it is detected at an early stage. However, the condition is frequently detected at an advanced stage in the elderly population. Therefore, early-stage detection may save patients from irreversible vision loss. The manual assessment of glaucoma by ophthalmologists includes various skill-oriented, costly, and time-consuming methods. Several techniques are in experimental stages to detect early-stage glaucoma, but a definite diagnostic technique remains elusive. We present an automatic method based on deep learning that can detect early-stage glaucoma with very high accuracy. The detection technique involves the identification of patterns from the retinal images that are often overlooked by clinicians. The proposed approach uses the gray channels of fundus images and applies the data augmentation technique to create a large dataset of versatile fundus images to train the convolutional neural network model. Using the ResNet-50 architecture, the proposed approach achieved excellent results for detecting glaucoma on the G1020, RIM-ONE, ORIGA, and DRISHTI-GS datasets. We obtained a detection accuracy of 98.48%, a sensitivity of 99.30%, a specificity of 96.52%, an AUC of 97%, and an F1-score of 98% by using the proposed model on the G1020 dataset. The proposed model may help clinicians to diagnose early-stage glaucoma with very high accuracy for timely interventions.

The Central Serous Retinopathy (CSR) or Central Serous Chorioretinopathy (CSC) is a significant disease that causes blindness and vision loss among in millions of people in the world. It transpires as a result of accumulation of watery fluids behind the retina. Therefore, detection of CSR at early stages allows taking preventive measures to avert any impairment to the human eye. Traditionally, several manual methods for detecting CSR have been developed in the past; however, they have shown to be imprecise and unreliable. Consequently, due to Artificial Intelligence (AI) services in the medical field, including automated CSR detection, it is now possible to detect and cure this disease. This review assessed a variety of innovative technologies and researches that contribute to the automatic detection of CSR. In this review, various CSR disease detection techniques, broadly classified into two categories: a) CSR detection based on classical imaging technologies, and b) CSR detection based on Machine/Deep Learning methods, have been reviewed after an elaborated evaluation of 29 different relevant articles. Additionally, it also goes over the advantages, drawbacks and limitations of a variety of traditional imaging techniques, such as Optical Coherence Tomography Angiography (OCTA), Fundus Imaging and more recent approaches that utilize Artificial Intelligence techniques. Finally, it is concluded that the most recent Deep Learning (DL) classifiers are found to deliver accurate, fast, and reliable CSR detection. However, there is a need of more researches on publically available datasets to improve computation complexity for the reliable detection and diagnosis of CSR disease.

The human eye is responsible for the visual reorganization of objects in the environment. The eye is divided into different layers and front/back areas; however, the most important part is the retina, responsible for capturing light and generating electrical impulses for further processing in the brain. Several manual and automated methods have been proposed to detect retinal diseases, though these techniques are time-consuming, inefficient, and unpleasant for patients. This research proposes a deep learning-based CSR detection employing two imaging techniques: OCT and fundus photography. These input images are manually augmented before classification, followed by training of DarkNet and DenseNet networks through both datasets. Moreover, pre-trained DarkNet and DenseNet classifiers are modified according to the need. Finally, the performance of both networks on their datasets is compared using evaluation parameters. After several experiments, the best accuracy of 99.78%, the sensitivity of 99.6%, specificity of 100%, and the F1 score of 99.52% were achieved through OCT images using the DenseNet network. The experimental results demonstrate that the proposed model is effective and efficient for CSR detection using the OCT dataset and suitable for deployment in clinical applications.