Diabetic and Hypertensive Retinopathy Screening in Fundus Images Using Artificially Intelligent Shallow Architectures

Retinal blood vessels are considered valuable biomarkers for the detection of diabetic retinopathy, hypertensive retinopathy, and other retinal disorders. Ophthalmologists analyze retinal vasculature by manual segmentation, which is a tedious task. Numerous studies have focused on automatic retinal vasculature segmentation using different methods for ophthalmic disease analysis. However, most of these methods are computationally expensive and lack robustness. This paper proposes two new shallow deep learning architectures: dual-stream fusion network (DSF-Net) and dual-stream aggregation network (DSA-Net) to accurately detect retinal vasculature. The proposed method uses semantic segmentation in raw color fundus images for the screening of diabetic and hypertensive retinopathies. The proposed method’s performance is assessed using three publicly available fundus image datasets: Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of Retina (STARE), and Children Heart Health Study in England Database (CHASE-DB1). The experimental results revealed that the proposed method provided superior segmentation performance with accuracy (Acc), sensitivity (SE), specificity (SP), and area under the curve (AUC) of 96.93%, 82.68%, 98.30%, and 98.42% for DRIVE, 97.25%, 82.22%, 98.38%, and 98.15% for CHASE-DB1, and 97.00%, 86.07%, 98.00%, and 98.65% for STARE datasets, respectively. The experimental results also show that the proposed DSA-Net provides higher SE compared to the existing approaches. It means that the proposed method detected the minor vessels and provided the least false negatives, which is extremely important for diagnosis. The proposed method provides an automatic and accurate segmentation mask that can be used to highlight the vessel pixels. This detected vasculature can be utilized to compute the ratio between the vessel and the non-vessel pixels and distinguish between diabetic and hypertensive retinopathies, and morphology can be analyzed for related retinal disorders.

Retinal Blood Vessel Extraction Using a New Enhancement Technique of Modified Convolution Filters and Sauvola Thresholding

The retinal blood vessels in humans are major components with different shapes and sizes. The extraction of the blood vessels from the retina is an important step to identify the type or nature of the pattern of the diseases in the retina. Furthermore, the retinal blood vessel was also used for diagnosis, detection, and classification. The most recent solution in this topic is to enable retinal image improvement or enhancement by a convolution filter and Sauvola threshold. In image enhancement, gamma correction is applied before filtering the retinal fundus. After that, the image should be transformed to a gray channel to enhance pictorial clarity using contrast-limited histogram equalization. For filter, this paper combines two convolution filters, namely sharpen and smooth filters. The Sauvola threshold, the morphology, and the medium filter are applied to extract blood vessels from the retinal image. This paper uses DRIVE and STARE datasets. The accuracies of the proposed method are 95.37% for DRIVE with a runtime of 1.77[Formula: see text]s and 95.17% for STARE with 2.05[Formula: see text]s runtime. Based on the result, it concludes that the proposed method is good enough to achieve average calculation parameters of a low time quality, quick, and significant.

Deep Att-ResGAN: A Retinal Vessel Segmentation Network for Color Fundus Images

Retinal vessel segmentation plays a significant role in the diagnosis and treatment of ophthalmological diseases. Recent studies have proved that deep learning can effectively segment the retinal vessel structure. However, the existing methods have difficulty in segmenting thin vessels, especially when the original image contains lesions. Based on generative adversarial network (GAN), this paper proposes a deep network with residual module and attention module (Deep Att-ResGAN). The network consists of four identical subnetworks. The output of each subnetwork is imported to the next subnetwork as contextual features that guide the segmentation. Firstly, the problems of the original image, namely, low contrast, uneven illumination, and data insufficiency, were solved through image enhancement and preprocessing. Next, an improved U-Net was adopted to serve as the generator, which stacks the residual and attention modules. These modules optimize the weight of the generator, and enhance the generalizability of the network. Further, the segmentation was refined iteratively by the discriminator, which contributes to the performance of vessel segmentation. Finally, comparative experiments were carried out on two public datasets: Digital Retinal Images for Vessel Extraction (DRIVE) and Structured Analysis of the Retina (STARE). The experimental results show that Deep Att-ResGAN outperformed the equivalent models like U-Net and GAN in most metrics. Our network achieved accuracy of 0.9565 and F1 of 0.829 on DRIVE, and accuracy of 0.9690 and F1 of 0.841 on STARE.

Modelling of Retinal Images for Analysis of Diabetic Retinopathy Severity Levels

Synthetic data by various algorithms that resemble actual data in terms of statistical features. Computer-aided medical applications have been extensively applied to model specific scenarios, such as medical imaging of retinal images for diabetic retinopathy (DR) detection. The available data and annotated medical data are typically rare and costly due to the difficulties of conducting medical screening and rely on highly trained doctors to review and diagnose. The modelling of retinal images for DR analysis is essential since it will provide a model to guide and test DR detection algorithms. This paper aims to model normal retina and non-proliferative diabetic retinopathy (NPDR) stages (mild, moderate, and severe) data models with the variation of dynamic models. The Digital Retinal Images for Vessel Extraction (DRIVE), The Standard Diabetic Retinopathy Database, Calibration Level 1 (DIARETDB1), and E-OPHTHA datasets are analyzed to obtain the specification of the human retina and DR lesions. In the data modelling phases, the model includes the bright and dark retinal lesions with the variation of dynamic parameters. 4100 synthetic images are used where 200 normal images and 3900 NPDR images to test the performance of DR detection algorithms over the full range of parameters.

ATTEMPTING AN INDEPTH ANALYSIS OF RETINAL IMAGE SEGMENTATION

These days eye weaknesses are a typical issue in all age group individuals begins from a newborn child to mature age. The discovery and extraction of these infections is a troublesome and tedious assignment. Computerized retinal pictures are considered; the first important strategy is to separate vessel in fundus pictures. Thus, three methods are utilized regulated techniques; here, the training set applies to remove vessel data by the pre-trained algorithm. This strategy is physically dealt with using gold std; vessel extraction is done before pathology calculations are involved in unaided recognition and extraction programs. The preparation set and ground truth marking will not be straightforwardly appropriate to the analysis. Retinal vessels extraction is improving as a result of noninvasive imaging of the retinal pictures likewise the information got from the design of the vasculature, and this data is essential for the identification and analysis of a fundus picture retinal sicknesses and pathologies, which incorporates glaucoma, hypertension, Diabetics Retina chart, and Age-based Macula De-age. Quick division calculations can recognize these.

Dilated Deep Neural Architectures for Improving Retinal Vessel Extraction

Human eye is an absolute sensory organ for vision. Eye sight is entirely accomplished by the blood flow in retinal vessels in eye. Diseases such as diabetes retinopathy, hypertension and arteriosclerosis cause change in branching pattern and diameter of retinal blood vessels leading to blindness. These changes can be analyzed by segmenting retinal blood vessel. Hence the retinal vasculature is recognized as the promising anatomical region for the diagnosis of several commonly seen diseases including cardiovascular related and diabetes. In this paper we propose two novel deep neural architectures named as Dilated fully convolved convolutional neural network (FCNN) and dilated depth concatenated neural network (DCNN) to segment the retinal blood vessels. The feature maps of fundus images are extracted by multiple dilated convolutional layers and due to the large field of view by dilation, pixel classification gets improved. The proposed work is evaluated for both the proposed architectures with and without dilation. It is observed from the obtained results that dilation enhances the network performance. To eliminate the non-uniform illumination and low contrast differences effect the preprocessed images are used for training the architectures. The proposed methodologies are experimented on the two publicly available databases DRIVE and STARE database.