A generalized method for the segmentation of exudates from pathological retinal fundus images

Prior-guided attention fusion transformer for multi-lesion segmentation of diabetic retinopathy

To solve the issue of diagnosis accuracy of diabetic retinopathy (DR) and reduce the workload of ophthalmologists, in this paper we propose a prior-guided attention fusion Transformer for multi-lesion segmentation of DR. An attention fusion module is proposed to improve the key generator to integrate self-attention and cross-attention and reduce the introduction of noise. The self-attention focuses on lesions themselves, capturing the correlation of lesions at a global scale, while the cross-attention, using pre-trained vessel masks as prior knowledge, utilizes the correlation between lesions and vessels to reduce the ambiguity of lesion detection caused by complex fundus structures. A shift block is introduced to expand association areas between lesions and vessels further and to enhance the sensitivity of the model to small-scale structures. To dynamically adjust the model's perception of features at different scales, we propose the scale-adaptive attention to adaptively learn fusion weights of feature maps at different scales in the decoder, capturing features and details more effectively. The experimental results on two public datasets (DDR and IDRiD) demonstrate that our model outperforms other state-of-the-art models for multi-lesion segmentation.

Automated Detection and Segmentation of Exudates for the Screening of Background Retinopathy

Exudate, an asymptomatic yellow deposit on retina, is among the primary characteristics of background diabetic retinopathy. Background diabetic retinopathy is a retinopathy related to high blood sugar levels which slowly affects all the organs of the body. The early detection of exudates aids doctors in screening the patients suffering from background diabetic retinopathy. A computer-aided method proposed in the present work detects and then segments the exudates in the images of retina acquired using a digital fundus camera by (i) gradient method to trace the contour of exudates, (ii) marking the connected candidate pixels to remove false exudates pixels, and (iii) linking the edge pixels for the boundary extraction of exudates. The method is tested on 1307 retinal fundus images with varying characteristics. Six hundred and forty-nine images were acquired from hospital and the remaining 658 from open-source benchmark databases, namely, STARE, DRIVE MESSIDOR, DiaretDB1, and e-Ophtha. The exudates segmentation method proposed in this research work results in the retinal fundus image-based (i) accuracy of 98.04%, (ii) sensitivity of 95.345%, and (iii) specificity of 98.63%. The segmentation results for a number of exudates-based evaluations depict the average (i) accuracy of 95.68%, (ii) sensitivity of 93.44%, and (iii) specificity of 97.22%. The substantial combined performance at image and exudates-based evaluations proves the contribution of the proposed method in mass screening as well as treatment process of background diabetic retinopathy.

Exudate identification in retinal fundus images using precise textural verifications

One of the most salient diseases of retina is Diabetic Retinopathy (DR) which may lead to irreparable damages to eye vision in the advanced phases. A large number of the people infected with diabetes experience DR. The early identification of DR signs facilitates the treatment process and prevents from blindness. Hard Exudates (HE) are bright lesions appeared in retinal fundus images of DR patients. Thus, the detection of HEs is an important task preventing the progress of DR. However, the detection of HEs is a challenging process due to their different appearance features. In this paper, an automatic method for the identification of HEs with various sizes and shapes is proposed. The method works based on a pixel-wise approach. It considers several semi-circular regions around each pixel. For each semi-circular region, the intensity changes around several directions and non-necessarily equal radiuses are computed. All pixels for which several semi-circular regions include considerable intensity changes are considered as the pixels located in HEs. In order to reduce false positives, an optic disc localization method is proposed in the post-processing phase. The performance of the proposed method has been evaluated on DIARETDB0 and DIARETDB1 datasets. The experimental results confirm the improved performance of the suggested method in term of accuracy.

Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods

The exponential increase in the number of diabetics around the world has led to an equally large increase in the number of diabetic retinopathy (DR) cases which is one of the major complications caused by diabetes. Left unattended, DR worsens the vision and would lead to partial or complete blindness. As the number of diabetics continue to increase exponentially in the coming years, the number of qualified ophthalmologists need to increase in tandem in order to meet the demand for screening of the growing number of diabetic patients. This makes it pertinent to develop ways to automate the detection process of DR. A computer aided diagnosis system has the potential to significantly reduce the burden currently placed on the ophthalmologists. Hence, this review paper is presented with the aim of summarizing, classifying, and analyzing all the recent development on automated DR detection using fundus images from 2015 up to this date. Such work offers an unprecedentedly thorough review of all the recent works on DR, which will potentially increase the understanding of all the recent studies on automated DR detection, particularly on those that deploys machine learning algorithms. Firstly, in this paper, a comprehensive state-of-the-art review of the methods that have been introduced in the detection of DR is presented, with a focus on machine learning models such as convolutional neural networks (CNN) and artificial neural networks (ANN) and various hybrid models. Each AI will then be classified according to its type (e.g. CNN, ANN, SVM), its specific task(s) in performing DR detection. In particular, the models that deploy CNN will be further analyzed and classified according to some important properties of the respective CNN architectures of each model. A total of 150 research articles related to the aforementioned areas that were published in the recent 5 years have been utilized in this review to provide a comprehensive overview of the latest developments in the detection of DR.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10462-022-10185-6.

Recent trends and advances in fundus image analysis: A review

Automated retinal image analysis holds prime significance in the accurate diagnosis of various critical eye diseases that include diabetic retinopathy (DR), age-related macular degeneration (AMD), atherosclerosis, and glaucoma. Manual diagnosis of retinal diseases by ophthalmologists takes time, effort, and financial resources, and is prone to error, in comparison to computer-aided diagnosis systems. In this context, robust classification and segmentation of retinal images are primary operations that aid clinicians in the early screening of patients to ensure the prevention and/or treatment of these diseases. This paper conducts an extensive review of the state-of-the-art methods for the detection and segmentation of retinal image features. Existing notable techniques for the detection of retinal features are categorized into essential groups and compared in depth. Additionally, a summary of quantifiable performance measures for various important stages of retinal image analysis, such as image acquisition and preprocessing, is provided. Finally, the widely used in the literature datasets for analyzing retinal images are described and their significance is emphasized.

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Diabetes is a long-term condition in which the pancreas quits producing insulin or the body's insulin isn't utilised properly. One of the signs of diabetes is Diabetic Retinopathy. Diabetic retinopathy is the most prevalent type of diabetes, if remains unaddressed, diabetic retinopathy can affect all diabetics and become very serious, raising the chances of blindness. It is a chronic systemic condition that affects up to 80% of patients for more than ten years. Many researchers believe that if diabetes individuals are diagnosed early enough, they can be rescued from the condition in 90% of cases. Diabetes damages the capillaries, which are microscopic blood vessels in the retina. On images, blood vessel damage is usually noticeable. Therefore, in this study, several traditional, as well as deep learning-based approaches, are reviewed for the classification and detection of this particular diabetic-based eye disease known as diabetic retinopathy, and also the advantage of one approach over the other is also described. Along with the approaches, the dataset and the evaluation metrics useful for DR detection and classification are also discussed. The main finding of this study is to aware researchers about the different challenges occurs while detecting diabetic retinopathy using computer vision, deep learning techniques. Therefore, a purpose of this review paper is to sum up all the major aspects while detecting DR like lesion identification, classification and segmentation, security attacks on the deep learning models, proper categorization of datasets and evaluation metrics. As deep learning models are quite expensive and more prone to security attacks thus, in future it is advisable to develop a refined, reliable and robust model which overcomes all these aspects which are commonly found while designing deep learning models.

Investigations of CNN for Medical Image Analysis for Illness Prediction

When it comes to diabetic retinopathy, exudates are the most common sign; alarms for early screening and diagnosis are suggested. The images taken by cameras and high-definition ophthalmoscopes are riddled with flaws and noise. Overcoming noise difficulties and pursuing automated/computer-aided diagnosis is always a challenge. The major objective of this approach is to obtain a better prediction rate of diabetic retinopathy analysis. The accuracy, sensitivity, specificity, and prediction rate improvement are focused on the objective view. The images are separated into relevant patches of various sizes and stacked for use as inputs to CNN, which is then trained, tested, and validated. The article presents a mathematical approach to determine the prevalence, shape in precise, color, and density in the populations among image patches to operate and discover the fact the image collection consists of symptoms of exudates and methods to comprehend the diagnosis and suggest risks of early hospital treatment. The experimental result analysis of malignant quality shows the accuracy, sensitivity, specificity, and predictive value. Here, 78% of accuracy, 78.8% of sensitivity, and 78.3% of specificity are obtained, and both positive and negative predictive values are obtained.

Fundus image segmentation via hierarchical feature learning

Fundus Image Segmentation (FIS) is an essential procedure for the automated diagnosis of ophthalmic diseases. Recently, deep fully convolutional networks have been widely used for FIS with state-of-the-art performance. The representative deep model is the U-Net, which follows an encoder-decoder architecture. I believe it is suboptimal for FIS because consecutive pooling operations in the encoder lead to low-resolution representation and loss of detailed spatial information, which is particularly important for the segmentation of tiny vessels and lesions. Motivated by this, a high-resolution hierarchical network (HHNet) is proposed to learn semantic-rich high-resolution representations and preserve spatial details simultaneously. Specifically, a High-resolution Feature Learning (HFL) module with increasing dilation rates was first designed to learn the high-level high-resolution representations. Then, the HHNet was constructed by incorporating three HFL modules and two feature aggregation modules. The HHNet runs in a coarse-to-fine manner, and fine segmentation maps are output at the last level. Extensive experiments were conducted on fundus lesion segmentation, vessel segmentation, and optic cup segmentation. The experimental results reveal that the proposed method shows highly competitive or even superior performance in terms of segmentation performance and computation cost, indicating its potential advantages in clinical application.

Dual-Branch Network with Dual-Sampling Modulated Dice Loss for Hard Exudate Segmentation in Colour Fundus Images

Automated segmentation of hard exudates in colour fundus images is a challenge task due to issues of extreme class imbalance and enormous size variation. This paper aims to tackle these issues and proposes a dual-branch network with dual-sampling modulated Dice loss. It consists of two branches: large hard exudate biased segmentation branch and small hard exudate biased segmentation branch. Both of them are responsible for their own duties separately. Furthermore, we propose a dual-sampling modulated Dice loss for the training such that our proposed dual-branch network is able to segment hard exudates in different sizes. In detail, for the first branch, we use a uniform sampler to sample pixels from predicted segmentation mask for Dice loss calculation, which leads to this branch naturally be biased in favour of large hard exudates as Dice loss generates larger cost on misidentification of large hard exudates than small hard exudates. For the second branch, we use a re-balanced sampler to oversample hard exudate pixels and undersample background pixels for loss calculation. In this way, cost on misidentification of small hard exudates is enlarged, which enforces the parameters in the second branch fit small hard exudates well. Considering that large hard exudates are much easier to be correctly identified than small hard exudates, we propose an easy-to-difficult learning strategy by adaptively modulating the losses of two branches. We evaluate our proposed method on two public datasets and results demonstrate that ours achieves state-of-the-art performance.

Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this purpose, both fundus and optical coherence tomography (OCT) images are used to image the retina. Next, Deep-learning (DL)-/machine-learning (ML)-based approaches make it possible to extract features from the images and to detect the presence of DR, grade its severity and segment associated lesions. This review covers the literature dealing with AI approaches to DR such as ML and DL in classification and segmentation that have been published in the open literature within six years (2016-2021). In addition, a comprehensive list of available DR datasets is reported. This list was constructed using both the PICO (P-Patient, I-Intervention, C-Control, O-Outcome) and Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2009 search strategies. We summarize a total of 114 published articles which conformed to the scope of the review. In addition, a list of 43 major datasets is presented.

Fast and efficient retinal blood vessel segmentation method based on deep learning network

The segmentation of the retinal vascular tree presents a major step for detecting ocular pathologies. The clinical context expects higher segmentation performance with a reduced processing time. For higher accurate segmentation, several automated methods have been based on Deep Learning (DL) networks. However, the used convolutional layers bring to a higher computational complexity and so for execution times. For such need, this work presents a new DL based method for retinal vessel tree segmentation. Our main contribution consists in suggesting a new U-form DL architecture using lightweight convolution blocks in order to preserve a higher segmentation performance while reducing the computational complexity. As a second main contribution, preprocessing and data augmentation steps are proposed with respect to the retinal image and blood vessel characteristics. The proposed method is tested on DRIVE and STARE databases, which can achieve a better trade-off between the retinal blood vessel detection rate and the detection time with average accuracy of 0.978 and 0.98 in 0.59 s and 0.48 s per fundus image on GPU NVIDIA GTX 980 platforms, respectively for DRIVE and STARE database fundus images.

Survey on recent developments in automatic detection of diabetic retinopathy

Diabetic retinopathy (DR) is a disease facilitated by the rapid spread of diabetes worldwide. DR can blind diabetic individuals. Early detection of DR is essential to restoring vision and providing timely treatment. DR can be detected manually by an ophthalmologist, examining the retinal and fundus images to analyze the macula, morphological changes in blood vessels, hemorrhage, exudates, and/or microaneurysms. This is a time consuming, costly, and challenging task. An automated system can easily perform this function by using artificial intelligence, especially in screening for early DR. Recently, much state-of-the-art research relevant to the identification of DR has been reported. This article describes the current methods of detecting non-proliferative diabetic retinopathy, exudates, hemorrhage, and microaneurysms. In addition, the authors point out future directions in overcoming current challenges in the field of DR research.

Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy

Diabetic retinopathy (DR) is characterized by the presence of red lesions (RLs), such as microaneurysms and hemorrhages, and bright lesions, such as exudates (EXs). Early DR diagnosis is paramount to prevent serious sight damage. Computer-assisted diagnostic systems are based on the detection of those lesions through the analysis of fundus images. In this paper, a novel method is proposed for the automatic detection of RLs and EXs. As the main contribution, the fundus image was decomposed into various layers, including the lesion candidates, the reflective features of the retina, and the choroidal vasculature visible in tigroid retinas. We used a proprietary database containing 564 images, randomly divided into a training set and a test set, and the public database DiaretDB1 to verify the robustness of the algorithm. Lesion detection results were computed per pixel and per image. Using the proprietary database, 88.34% per-image accuracy (ACCi), 91.07% per-pixel positive predictive value (PPVp), and 85.25% per-pixel sensitivity (SEp) were reached for the detection of RLs. Using the public database, 90.16% ACCi, 96.26% PPV_p, and 84.79% SEp were obtained. As for the detection of EXs, 95.41% ACCi, 96.01% PPV_p, and 89.42% SE_p were reached with the proprietary database. Using the public database, 91.80% ACCi, 98.59% PPVp, and 91.65% SEp were obtained. The proposed method could be useful to aid in the diagnosis of DR, reducing the workload of specialists and improving the attention to diabetic patients.

Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning

Diabetic patients are at the risk of developing different eye diseases i.e., diabetic retinopathy (DR), diabetic macular edema (DME) and glaucoma. DR is an eye disease that harms the retina and DME is developed by the accumulation of fluid in the macula, while glaucoma damages the optic disk and causes vision loss in advanced stages. However, due to slow progression, the disease shows few signs in early stages, hence making disease detection a difficult task. Therefore, a fully automated system is required to support the detection and screening process at early stages. In this paper, an automated disease localization and segmentation approach based on Fast Region-based Convolutional Neural Network (FRCNN) algorithm with fuzzy k-means (FKM) clustering is presented. The FRCNN is an object detection approach that requires the bounding-box annotations to work; however, datasets do not provide them, therefore, we have generated these annotations through ground-truths. Afterward, FRCNN is trained over the annotated images for localization that are then segmented-out through FKM clustering. The segmented regions are then compared against the ground-truths through intersection-over-union operations. For performance evaluation, we used the Diaretdb1, MESSIDOR, ORIGA, DR-HAGIS, and HRF datasets. A rigorous comparison against the latest methods confirms the efficacy of the approach in terms of both disease detection and segmentation.

Intelligent Image Synthesis for Accurate Retinal Diagnosis

Ophthalmology is a core medical field that is of interest to many. Retinal examination is a commonly performed diagnostic procedure that can be used to inspect the interior of the eye and screen for any pathological symptoms. Although various types of eye examinations exist, there are many cases where it is difficult to identify the retinal condition of the patient accurately because the test image resolution is very low because of the utilization of simple methods. In this paper, we propose an image synthetic approach that reconstructs the vessel image based on past retinal image data using the multilayer perceptron concept with artificial neural networks. The approach proposed in this study can convert vessel images to vessel-centered images with clearer identification, even for low-resolution retinal images. To verify the proposed approach, we determined whether high-resolution vessel images could be extracted from low-resolution images through a statistical analysis using high- and low-resolution images extracted from the same patient.

Enhancement of blurry retinal image based on non-uniform contrast stretching and intensity transfer

Proper contrast and sufficient illuminance are important in clearly identifying the retinal structures, while the required quality cannot always be guaranteed due to major reasons like acquisition process and diseases. To ensure the effectiveness of enhancement, two solutions are developed for blurry retinal images with sufficient illuminance and insufficient illuminance, respectively. The proposed contrast stretching and intensity transfer are main steps in both of the two solutions. The contrast stretching is based on base-intensity removal and non-uniform addition. We assume that a base-intensity exists in an image, which mainly supports the basic illuminance but has less contribution to texture information. The base-intensity is estimated by the constrained Gaussian function and then removed. The non-uniform addition using compressed Gamma map is further developed to improve the contrast. Additionally, an effective intensity transfer strategy is introduced, which can provide required illuminance for a single channel after contrast stretching. The color correction can be achieved if the intensity transfer is performed on three channels. Results show that the proposed solutions can effectively improve the contrast and illuminance, and good visual perception for quality degraded retinal images is obtained. Illustration of contrast stretching based on a signal colour channel.

A Novel Weakly Supervised Multitask Architecture for Retinal Lesions Segmentation on Fundus Images

Obtaining the complete segmentation map of retinal lesions is the first step toward an automated diagnosis tool for retinopathy that is interpretable in its decision-making. However, the limited availability of ground truth lesion detection maps at a pixel level restricts the ability of deep segmentation neural networks to generalize over large databases. In this paper, we propose a novel approach for training a convolutional multi-task architecture with supervised learning and reinforcing it with weakly supervised learning. The architecture is simultaneously trained for three tasks: segmentation of red lesions and of bright lesions, those two tasks done concurrently with lesion detection. In addition, we propose and discuss the advantages of a new preprocessing method that guarantees the color consistency between the raw image and its enhanced version. Our complete system produces segmentations of both red and bright lesions. The method is validated at the pixel level and per-image using four databases and a cross-validation strategy. When evaluated on the task of screening for the presence or absence of lesions on the Messidor image set, the proposed method achieves an area under the ROC curve of 0.839, comparable with the state-of-the-art.

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

Exudates Segmentation using Fully Convolutional Neural Network and Auxiliary Codebook

Diabetic retinopathy (DR) is an asymptotic complication of diabetes and the leading cause of preventable blindness in the working-age population. Early detection and treatment of DR is critical to avoid vision loss. Exudates are one of the earliest and most prevalent signs of DR. In this work, we propose a novel two-stage method for the detection and segmentation of exudates in fundus photographs. In the first stage, a fully convolutional neural network architecture is trained to segment exudates using small image patches. Next, an auxilary codebook is built from network's intermediate layer output using incremental principal component analysis. Finally, outputs of both systems are combined to produce final result. Compared to other methods, the proposed algorithm does not require computation of candidate regions or removal of other anatomical structures. Furthermore, a transfer learning approach was applied to improve the performance of the system. The proposed method was evaluated using publicly available E-Ophtha datasets. It achieved better results than the state-of-the-art methods in terms of sensitivity and specificity metrics. The proposed method accomplished better results using a diseased//not diseased evaluation scenario which indicates its applicability for screening purposes. Simplicity, performance, efficiency and robustness of the proposed method demonstrate its suitability for diabetic retinopathy screening applications.

Diabetic retinopathy techniques in retinal images: A review

The diabetic retinopathy is the main reason of vision loss in people. Medical experts recognize some clinical, geometrical and haemodynamic features of diabetic retinopathy. These features include the blood vessel area, exudates, microaneurysm, hemorrhages and neovascularization, etc. In Computer Aided Diagnosis (CAD) systems, these features are detected in fundus images using computer vision techniques. In this paper, we review the methods of low, middle and high level vision for automatic detection and classification of diabetic retinopathy.We give a detailed review of 79 algorithms for detecting different features of diabetic retinopathy during the last eight years.

Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms

Background

Convolution neural networks have been considered for automatic analysis of fundus images to detect signs of diabetic retinopathy but suffer from low sensitivity.

Methods

This study has proposed an alternate method using probabilistic output from Convolution neural network to automatically and simultaneously detect exudates, hemorrhages and microaneurysms. The method was evaluated using two approaches: patch and image-based analysis of the fundus images on two public databases: DIARETDB1 and e-Ophtha. The novelty of the proposed method is that the images were analyzed using probability maps generated by score values of the softmax layer instead of the use of the binary output.

Results

The sensitivity of the proposed approach was 0.96, 0.84 and 0.85 for detection of exudates, hemorrhages and microaneurysms, respectively when considering patch-based analysis. The results show overall accuracy for DIARETDB1 was 97.3% and 86.6% for e-Ophtha. The error rate for image-based analysis was also significantly reduced when compared with other works.

Conclusion

The proposed method provides the framework for convolution neural network-based analysis of fundus images to identify exudates, hemorrhages, and microaneurysms. It obtained accuracy and sensitivity which were significantly better than the reported studies and makes it suitable for automatic diabetic retinopathy signs detection.

Exudate detection in fundus images using deeply-learnable features

Presence of exudates on a retina is an early sign of diabetic retinopathy, and automatic detection of these can improve the diagnosis of the disease. Convolutional Neural Networks (CNNs) have been used for automatic exudate detection, but with poor performance. This study has investigated different deep learning techniques to maximize the sensitivity and specificity. We have compared multiple deep learning methods, and both supervised and unsupervised classifiers for improving the performance of automatic exudate detection, i.e., CNNs, pre-trained Residual Networks (ResNet-50) and Discriminative Restricted Boltzmann Machines. The experiments were conducted on two publicly available databases: (i) DIARETDB1 and (ii) e-Ophtha. The results show that ResNet-50 with Support Vector Machines outperformed other networks with an accuracy and sensitivity of 98% and 0.99, respectively. This shows that ResNet-50 can be used for the analysis of the fundus images to detect exudates.

Artificial intelligence in retina

Major advances in diagnostic technologies are offering unprecedented insight into the condition of the retina and beyond ocular disease. Digital images providing millions of morphological datasets can fast and non-invasively be analyzed in a comprehensive manner using artificial intelligence (AI). Methods based on machine learning (ML) and particularly deep learning (DL) are able to identify, localize and quantify pathological features in almost every macular and retinal disease. Convolutional neural networks thereby mimic the path of the human brain for object recognition through learning of pathological features from training sets, supervised ML, or even extrapolation from patterns recognized independently, unsupervised ML. The methods of AI-based retinal analyses are diverse and differ widely in their applicability, interpretability and reliability in different datasets and diseases. Fully automated AI-based systems have recently been approved for screening of diabetic retinopathy (DR). The overall potential of ML/DL includes screening, diagnostic grading as well as guidance of therapy with automated detection of disease activity, recurrences, quantification of therapeutic effects and identification of relevant targets for novel therapeutic approaches. Prediction and prognostic conclusions further expand the potential benefit of AI in retina which will enable personalized health care as well as large scale management and will empower the ophthalmologist to provide high quality diagnosis/therapy and successfully deal with the complexity of 21st century ophthalmology.

Introducing a Novel Layer in Convolutional Neural Network for Automatic Identification of Diabetic Retinopathy

Convolutional neural networks have been widely used for identifying diabetic retinopathy on color fundus images. For such application, we proposed a novel framework for the convolutional neural network architecture by embedding a preprocessing layer followed by the first convolutional layer to increase the performance of the convolutional neural network classifier. Two image enhancement techniques i.e. 1- Contrast Enhancement 2- Contrast-limited adaptive histogram equalization were separately embedded in the proposed layer and the results were compared. For identification of exudates, hemorrhages and microaneurysms, the proposed framework achieved the total accuracy of 87.6%, and 83.9% for the contrast enhancement and contrast-limited adaptive histogram equalization layers, respectively. However, the total accuracy of the convolutional neural network alone without the prreprocessing layer was found to be 81.4%. Consequently, the new convolutional neural network architecture with the proposed preprocessing layer improved the performance of convolutional neural network.