Diagnosis Of Diabetic Retinopathy Research Articles

Deep Learning Empowered Diabetic Retinopathy Detection and Classification using Retinal Fundus Images

Diabetic Retinopathy (DR) is a commonly occurring disease among diabetic patients that affects retina lesions and vision. Since DR is irreversible, an earlier diagnosis of DR can considerably decrease the risk of vision loss. Manual detection and classification of DR from retinal fundus images is time-consuming, expensive, and prone to errors, contrasting to CAD models. In recent times, DL models have become a familiar topic in several applications, particularly medical image classification. With this motivation, this paper presents new deep learning-empowered diabetic retinopathy detection and classification (DL-DRDC) model. The DL-DRDC technique aims to recognize and categorize different grades of DR using retinal fundus images. The proposed model involves the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique as a pre-processing stage, which is used to enhance the contrast of the fundus images and improve the low contrast of medical images. Besides, the CLAHE is applied to the L channel of the retina images that have higher contrast. In addition, a deep learning-based Efficient Net-based feature extractor is used to generate feature vectors from pre-processed images. Moreover, a deep neural network (DNN) is used as a classifier model to allocate proper DR stages. An extensive set of experimental analyses takes place using a benchmark MESSIDOR dataset and the results are examined interms of different evaluation parameters. The simulation values highlighted the better DR diagnostic efficiency of the DL-DRDC technique over the recent techniques.

Association of socioeconomic status with glycated haemoglobin level and risk factors for diabetic retinopathy: a cross-sectional study in Sichuan, western China

ObjectiveDiabetes affects 1 in 10 adults in China. Diabetic retinopathy (DR) is a diabetes-related complication that, if untreated, impairs vision and causes blindness. Evidence on DR diagnosis and risk factors...

A Wrapped Approach Using Unlabeled Data for Diabetic Retinopathy Diagnosis

Large-scale datasets, which have sufficient and identical quantities of data in each class, are the main factor in the success of deep-learning-based classification models for vision tasks. A shortage of sufficient data and interclass imbalanced data distribution, which often arise in the medical domain, cause modern deep neural networks to suffer greatly from imbalanced learning and overfitting. A diagnostic model of diabetic retinopathy (DR) that is trained from such a dataset using supervised learning is severely biased toward the majority class. To enhance the efficiency of imbalanced learning, the proposal of this study is to leverage retinal fundus images without human annotations by self-supervised or semi-supervised learning. The proposed approach to DR detection is to add an auxiliary procedure to the target task that identifies DR using supervised learning. The added process uses unlabeled data to pre-train the model that first learns features from data using self-supervised or semi-supervised learning, and then the pre-trained model is transferred with the learned parameter to the target model. This wrapper algorithm of learning from unlabeled data can help the model gain more information from samples in the minority class, thereby improving imbalanced learning to some extent. Comprehensive experiments demonstrate that the model trained with the proposed method outperformed the one trained with only the supervised learning baseline utilizing the same data, with an accuracy improvement of 4~5%. To further examine the method proposed in this study, a comparison is conducted, and our results show that the proposed method also performs much better than some state-of-the-art methods. In the case of EyePaCS, for example, the proposed method outperforms the customized CNN model by 9%. Through experiments, we further find that the models trained with a smaller but balanced dataset are not worse than those trained with a larger but imbalanced dataset. Therefore, our study reveals that utilizing unlabeled data can avoid the expensive cost of collecting and labeling large-scale medical datasets.

Pathophysiology and diagnosis of diabetic retinopathy: a narrative review.

Diabetes is an endocrine disorder which is known by abnormal high blood glucose levels. There are two main categories of diabetes: type I (10%-15%) and type II (85%-90%). Although type II is more common, type I is the most common form in children. Diabetic retinopathy (DR), which remains the foremost cause of losing vision in working-age populations, can be considered as the main complication of diabetes mellitus. So choosing the best method for diagnosing, tracking, and treating the DR is vital to enhance the quality of life and decrease the medical expenses. Each method for diagnosing DR has some advantages and the best way must be selected according to the points that we need to find. For writing this manuscript, we made a list of relevant keywords including diabetes, DR, pathophysiology, ultrawide field imaging, fluorescein angiography, optical coherence tomography, and optical coherence tomography-angiography, and then we started searching for studies in PubMed, Scopus, and Web of Science databases. This review article covers the pathophysiology of DR and medical imaging techniques to monitor DR. First, we introduce DR and its pathophysiology and then we present the medical imaging techniques to monitor it.

Association of Diabetic Retinopathy with Angiographic Severity of Coronary Artery Disease in Patients with Non-ST Elevation Myocardial Infarction

Background: Bed side ophthalmoscopic examination is a simple measure of diagnosis of diabetic retinopathy and has been shown to be a predictor of poor outcome in various cardiovascular conditions including coronary artery disease (CAD). Retinopathy lesions are fairly common findings in clinic settings and may predict risk of coronary heart disease (CHD). The present study was intended to find the relationship between diabetic retinopathy with the severity of coronary artery disease in patients with NSTEMI. Methods: This cross-sectional observational study was conducted in the Department of cardiology, National Institute of Cardiovascular Diseases and Hospital (NICVD), Dhaka, Bangladesh, from March 2019 to August 2020. A total of one hundred twenty DM with NSTEMI patients undergoing coronary angiogram and also fundoscopic examination with fundal photography during the index hospitalization were included in this study. Study subjects were divided into two groups on the basis of diabetic retinopathy (Group-I: NSTEMI with diabetic retinopathy ; Group- II: NSTEMI without diabetic retinopathy). Severity of coronary artery disease was determined by Gensini score and correlation between diabetic retinopathy and Gensini score was assessed. Results: Gensini score was significantly higher in patients with diabetic retinopathy than that in patients without diabetic retinopathy (62.2±27.7 vs. 43.3±25.3,p<0.001).Gensini score increased with increasing severity of diabetic retinopathy(P <0.001).The risk of having severe CAD in patient with diabetic retinopathy was 13.03 (95% CI =2.410-70.419) (P<0.003).A significant correlation between diabetic retinopathy and Gensini score was noted (p value <0.001) Conclusion: It may be concluded that Presence and severity of diabetic retinopathy is associated with angiographically severe coronary artery disease in patient with non-ST elevation myocardial infarction (NSTEMI) and it may be considered as an independent predictor of severity of CAD.As is a bed side assessment ,so before performing coronary angiography ,it appears to be additive for risk stratification. J Inv Clin Cardiol 2023; 5(1): 7-13

Health Care Disparities in Diabetes and Diabetic Retinopathy

ABSTRACT Purpose To investigate prevalence of diabetes (DM), diabetic retinopathy (DR), and areas with highest rates of undetected DR. To quantify and map locations of disparities as they relate to poverty and minority populations. Methods Retrospective cohort study from large regional health data repository (HealthLNK). Geographic Information System (GIS) analysis mapped rates of DM and DR in Chicago area ZIP Codes. Results Of 1,086,921 adults who met the inclusion criteria, 143,790 with DM were identified. ZIP Codes with higher poverty rates were correlated with higher prevalence of DM and DR (Pearson’s correlation coefficient 0.614, p < .05, 0.333, p < .05). Poverty was negatively correlated with likelihood of DR diagnosis (−0.638, p < .05). Relative risks of DM and DR were calculated in each ZIP Code and compared to actual rates. 36 high-risk ZIP Codes had both high-risk of DM and low DR detection. In high-risk ZIP Codes 85.4% of households self-identified as ethnic minority and 33.0% were below the Federal Poverty Level (FPL). Both percentages were significantly higher than the Chicago average of 50.5% minority and 19.9% below FPL (p < .05). 67 ideal ZIP Codes had both low risk of DM and high DR detection. In ideal ZIP Codes 32.6% of households self-identified as minority, and 10.2% were below the FPL (p < .05). Conclusions A health care disparity exists with regards to DM and DR. High-risk ZIP Codes are associated with higher poverty and higher minority population, and they are highly concentrated in just 17% of the ZIP codes in the Chicago area.

Diabetic Retinopathy: Soluble and Imaging Ocular Biomarkers.

Diabetic retinopathy (DR), the most common microvascular complication of diabetes mellitus, represents the leading cause of acquired blindness in the working-age population. Due to the potential absence of symptoms in the early stages of the disease, the identification of clinical biomarkers can have a crucial role in the early diagnosis of DR as well as for the detection of prognostic factors. In particular, imaging techniques are fundamental tools for screening, diagnosis, classification, monitoring, treatment planning and prognostic assessment in DR. In this context, the identification of ocular and systemic biomarkers is crucial to facilitate the risk stratification of diabetic patients; moreover, reliable biomarkers could provide prognostic information on disease progression as well as assist in predicting a patient's response to therapy. In this context, this review aimed to provide an updated and comprehensive overview of the soluble and anatomical biomarkers associated with DR.

A Survey on Deep-Learning-Based Diabetic Retinopathy Classification.

The number of people who suffer from diabetes in the world has been considerably increasing recently. It affects people of all ages. People who have had diabetes for a long time are affected by a condition called Diabetic Retinopathy (DR), which damages the eyes. Automatic detection using new technologies for early detection can help avoid complications such as the loss of vision. Currently, with the development of Artificial Intelligence (AI) techniques, especially Deep Learning (DL), DL-based methods are widely preferred for developing DR detection systems. For this purpose, this study surveyed the existing literature on diabetic retinopathy diagnoses from fundus images using deep learning and provides a brief description of the current DL techniques that are used by researchers in this field. After that, this study lists some of the commonly used datasets. This is followed by a performance comparison of these reviewed methods with respect to some commonly used metrics in computer vision tasks.

Multiocular disease detection using a generic framework based on handcrafted and deep learned feature analysis

Diabetic retinopathy (DR) and Glaucoma are two major ocular diseases that lead to vision impairment if not detected and treated promptly. Manual detection and diagnosis of these diseases is a laborious and time-consuming process. Computer aided diagnosis (CAD) systems can serve to assist physicians in manual diagnosis. This work aims to develop a generic multiocular CAD system for DR and glaucoma diagnosis, which could serve as a boon in a large scale screening scenario by reducing the time and manpower involved. To this end, a segmentation-independent approach is employed that eliminates the need for individual diagnostic systems each involving a set of disease-specific algorithms for localisation and analysis of regions of interest (ROIs). Further, the proposed approach alleviates the segmentation inaccuracies attributed to image quality and anatomical factors, which have a cascaded effect on the classification performance. Specifically, a machine learning (ML) model based on random forest (RF) classifier and a pool of non-linear features including higher order spectra (HOS), entropy and fractal features, was developed. An ensemble of this ML model and convolutional neural network (CNN)-based deep learning (DL) model was further constructed using the ‘sum rule’ for decision fusion. The proposed ensemble model resulted in accuracy, sensitivity and specificity as high as 98.08%, 98.37% and 99.07% respectively, for three-class classification to categorize samples as normal, DR or glaucoma.

Diabetic retinopathy: Looking forward to 2030.

Diabetic retinopathy (DR) is the major ocular complication of diabetes mellitus, and is a problem with significant global health impact. Major advances in diagnostics, technology and treatment have already revolutionized how we manage DR in the early part of the 21st century. For example, the accessibility of imaging with optical coherence tomography, and the development of anti-vascular endothelial growth factor (VEGF) treatment are just some of the landmark developments that have shaped the DR landscape over the last few decades. Yet, there are still more exciting advances being made. Looking forward to 2030, many of these ongoing developments are likely to further transform the field. First, epidemiologic projections show that the global burden of DR is not only increasing, but also shifting from high-income countries towards middle- and low-income areas. Second, better understanding of disease pathophysiology is placing greater emphasis on retinal neural dysfunction and non-vascular aspects of diabetic retinal disease. Third, a wealth of information is becoming available from newer imaging modalities such as widefield imaging systems and optical coherence tomography angiography. Fourth, artificial intelligence for screening, diagnosis and prognostication of DR will become increasingly accessible and important. Fifth, new pharmacologic agents targeting other non-VEGF-driven pathways, and novel therapeutic strategies such as gene therapy are being developed for DR. Finally, the classification system for diabetic retinal disease will need to be continually updated to keep pace with new developments. In this article, we discuss these major trends in DR that we expect to see in 2030 and beyond.

Designing the Architecture of a Convolutional Neural Network Automatically for Diabetic Retinopathy Diagnosis

Diabetic retinopathy (DR) is a leading cause of blindness in middle-aged diabetic patients. Regular screening for DR using fundus imaging aids in detecting complications and delays the progression of the disease. Because manual screening takes time and is subjective, deep learning has been used to help graders. Pre-trained or brute force CNN models are used in existing DR grading CNN-based approaches that are not suited to fundus image complexity. To solve this problem, we present a method for automatically customizing CNN models based on fundus image lesions. It uses k-medoid clustering, principal component analysis (PCA), and inter-class and intra-class variations to determine the CNN model’s depth and width. The designed models are lightweight, adapted to the internal structures of fundus images, and encode the discriminative patterns of DR lesions. The technique is validated on a local dataset from King Saud University Medical City, Saudi Arabia, and two challenging Kaggle datasets: EyePACS and APTOS2019. The auto-designed models outperform well-known pre-trained CNN models such as ResNet152, DenseNet121, and ResNeSt50, as well as Google’s AutoML and Auto-Keras models based on neural architecture search (NAS). The proposed method outperforms current CNN-based DR screening methods. The proposed method can be used in various clinical settings to screen for DR and refer patients to ophthalmologists for further evaluation and treatment.

Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images

Recently, the Internet of Things (IoT) and computer vision technologies find useful in different applications, especially in healthcare. IoT driven healthcare solutions provide intelligent solutions for enabling substantial reduction of expenses and improvisation of healthcare service quality. At the same time, Diabetic Retinopathy (DR) can be described as permanent blindness and eyesight damage because of the diabetic condition in humans. Accurate and early detection of DR could decrease the loss of damage. Computer-Aided Diagnoses (CAD) model based on retinal fundus image is a powerful tool to help experts diagnose DR. Some traditional Machine Learning (ML) based DR diagnoses model has currently existed in this study. The recent developments of Deep Learning (DL) and its considerable achievement over conventional ML algorithms for different applications make it easier to design effectual DR diagnosis model. With this motivation, this paper presents a novel IoT and DL enabled diabetic retinopathy diagnosis model (IoTDL-DRD) using retinal fundus images. The presented Internet of Things Deep Learning – Diabetic Retinopathy Diagnosis (IoTDL-DRD) technique utilizes IoT devices for data collection purposes and then transfers them to the cloud server to process them. Followed by, the retinal fundus images are preprocessed to remove noise and improve contrast level. Next, mayfly optimization based region growing (MFORG) based segmentation technique is utilized to detect lesion regions in the fundus image. Moreover, densely connected network (DenseNet) based feature extractor and Long Short Term Memory (LSTM) based classifier is used for effective DR diagnosis. Furthermore, the parameter optimization of the LSTM method can be carried out by Honey Bee Optimization (HBO) algorithm. For evaluating the improved DR diagnostic outcomes of the IoTDL-DRD technique, a comprehensive set of simulations were carried out. A wide ranging comparison study reported the superior performance of the proposed method.

Meta Heuristic Fusion Model for Classification with Modified U-Net-based Segmentation

General cause of diabetes mellitus is Diabetic Retinopathy (DR), which outcomes in lesions on the retinas that impair vision. If it is not detected in time, the result is severe blindness issues. Regrettably, there is no treatment for DR. Early diagnosis and treatment of DR can greatly lower the risk of visual loss. In contrast to computer-aided diagnosis methods, the manual diagnosis of DR using retina fundus images is more time-consuming effort, and high cost as well, as it is highly prone to error. Deep learning has emerged as one of the most popular methods for improving performance, particularly in the classification and analysis of medical images. Therefore, a deep structure-based DR detection and severity classification has been demonstrated for treating the DR with the usage of fundus images. The major aim of this developed technique is to classify the severity level of the retinal region of the human eye from the fundus images. At first, the required retinal fundus images are collected from the standard benchmark data sources. Secondly, image enhancement techniques are applied to the collected fundus images to improve the quality of images. Thirdly, the abnormality segmentations are carried out by using the optic disc removal process using active contouring model and then, the regional segmentation is done via the Modified U-Net method. Finally, the segmented image is subjected to the hybrid classifier network named a Hybrid Soft Attention-based DenseNet with Multi-Scale Gated ResNet (HSADMGR Net) for classifying the retinal fundus images and finding the severity level of the retinal images with higher accuracy. Furthermore, the parameters present inside the hybrid classifier network are optimized with the help of implemented Multi-Armed Bandits Groundwater Flow Algorithm (MABGFA). The test results regarding the developed deep structure-based DR model are validated with the existing DR detection and classification approaches by using different performance measures.

Diabetic Retinopathy Diagnosis Using Interval Neutrosophic Segmentation with Deep Learning Model

In recent times, Internet of Things (IoT) and Deep Learning (DL) models have revolutionized the diagnostic procedures of Diabetic Retinopathy (DR) in its early stages that can save the patient from vision loss. At the same time, the recent advancements made in Machine Learning (ML) and DL models help in developing Computer Aided Diagnosis (CAD) models for DR recognition and grading. In this background, the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network (ODBN) model i.e., NS-ODBN model for diagnosis of DR. The presented model involves Interval Neutrosophic Set (INS) technique to distinguish the diseased areas in fundus image. In addition, three feature extraction techniques such as histogram features, texture features, and wavelet features are used in this study. Besides, Optimal Deep Belief Network (ODBN) model is utilized as a classification model for DR. ODBN model involves Shuffled Shepherd Optimization (SSO) algorithm to regulate the hyperparameters of DBN technique in an optimal manner. The utilization of SSO algorithm in DBN model helps in increasing the detection performance of the model significantly. The presented technique was experimentally evaluated using benchmark DR dataset and the results were validated under different evaluation metrics. The resultant values infer that the proposed INS-ODBN technique is a promising candidate than other existing techniques.

IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques

In recent years, there has been a significant increase in the number of people suffering from eye illnesses, which should be treated as soon as possible in order to avoid blindness. Retinal Fundus images are employed for this purpose, as well as for analysing eye abnormalities and diagnosing eye illnesses. Exudates can be recognised as bright lesions in fundus pictures, which can be the first indicator of diabetic retinopathy. With that in mind, the purpose of this work is to create an Integrated Model for Exudate and Diabetic Retinopathy Diagnosis (IM-EDRD) with multi-level classifications. The model uses Support Vector Machine (SVM)-based classification to separate normal and abnormal fundus images at the first level. The input pictures for SVM are pre-processed with Green Channel Extraction and the retrieved features are based on Gray Level Co-occurrence Matrix (GLCM). Furthermore, the presence of Exudate and Diabetic Retinopathy (DR) in fundus images is detected using the Adaptive Neuro Fuzzy Inference System (ANFIS) classifier at the second level of classification. Exudate detection, blood vessel extraction, and Optic Disc (OD) detection are all processed to achieve suitable results. Furthermore, the second level processing comprises Morphological Component Analysis (MCA) based image enhancement and object segmentation processes, as well as feature extraction for training the ANFIS classifier, to reliably diagnose DR. Furthermore, the findings reveal that the proposed model surpasses existing models in terms of accuracy, time efficiency, and precision rate with the lowest possible error rate.

Evaluation of the diagnostic capabilities of nailfold capillaroscopy in diabetic retinopathy

This study evaluates the diagnostic capabilities and the prognostic value of nailfold capillaroscopy data of patients with diabetic retinopathy (DR) to develop an algorithm of monitoring patients with type 2 diabetes mellitus. The study involved 90 patients (mean age 67 years), among them 31 with nonproliferative diabetic retinopathy, 29 patients with proliferative DR and 30 patients without retinopathy. In addition to conventional ophthalmological examination, optical coherence tomography angiography (OCTA) on the Optovue RTVue-100 device (USA) was performed using en face vessel density protocol to examine the state of the microvasculature of the superficial and deep layers of the vascular plexus of the central retinal zone, as well as nailfold capillaroscopy using computerized capillaroscope KK-01 (ZAO Centr Analiz veshhestv, Russia). The cut-off points for detecting the presence of non-proliferative DR (capillary network density below 38.4%, arterial velocity below 512 mm/s and venous blood flow below 585 mm/s), and the presence of proliferative DR (capillary network density below 30.4%, the arterial velocity below 451 mm/s and the venous blood flow below 441 mm/s) were identified according to ROC-analysis of nailfold capillaroscopy data. In the diagnosis of proliferative DR the capillary network density parameter has a slightly higher diagnostic information value (AUC=0.963) than arterial blood flow velocity (AUC=0.941) or venous blood flow velocity (AUC=0.909). Using the identified critical parameters for predicting the initial and proliferative DR, we created a diagnostic algorithm involving a comprehensive assessment of all characteristics. The study revealed that nailfold capillaroscopy indicators (capillary network density, velocity of arterial and venous blood flow) have high diagnostic information value for detecting both non-proliferative and proliferative retinopathy. We constructed mathematical models for predicting DR with an accuracy of predicting the presence of a non-proliferative stage in 92.2% of cases and a proliferative stage in 94.4% of cases. For practical use in clinical environment, we created a computer program calculating the results of DR predictions according to nailfold capillaroscopy data.

Machine Learning Based Diagnosis for Diabetic Retinopathy for SKPD-PSC

The study aimed to apply to Machine Learning (ML) researchers working in image processing and biomedical analysis who play an extensive role in comprehending and performing on complex medical data, eventually improving patient care. Developing a novel ML algorithm specific to Diabetic Retinopathy (DR) is a challenge and need of the hour. Biomedical images include several challenges, including relevant feature selection, class variations, and robust classification. Although the current research in DR has yielded favourable results, several research issues need to be explored. There is a requirement to look at novel pre-processing methods to discard irrelevant features, balance the obtained relevant features, and obtain a robust classification. This is performed using the Steerable Kernalized Partial Derivative and Platt Scale Classifier (SKPD-PSC) method. The novelty of this method relies on the appropriate non-linear classification of exclusive image processing models in harmony with the Platt Scale Classifier (PSC) to improve the accuracy of DR detection. First, a Steerable Filter Kernel Pre-processing (SFKP) model is applied to the Retinal Images (RI) to remove irrelevant and redundant features and extract more meaningful pathological features through Directional Derivatives of Gaussians (DDG). Next, the Partial Derivative Image Localization (PDIL) model is applied to the extracted features to localize candidate features and suppress the background noise. Finally, a Platt Scale Classifier (PSC) is applied to the localized features for robust classification. For the experiments, we used the publicly available DR detection database provided by Standard Diabetic Retinopathy (SDR), called DIARETDB0. A database of 130 image samples has been collected to train and test the ML-based classifiers. Experimental results show that the proposed method that combines the image processing and ML models can attain good detection performance with a high DR detection accuracy rate with minimum time and complexity compared to the state-of-the-art methods. The accuracy and speed of DR detection for numerous types of images will be tested through experimental evaluation. Compared to state-of-the-art methods, the method increases DR detection accuracy by 24% and DR detection time by 37.

Performance Comparison of CNN and DNN Algorithms for Automation of Diabetic Retinopathy Disease

Automation of medical image analysis helps medical practitioners to ensure early detection of certain diseases. Diabetic Retinopathy (DR) is a widespread condition of diabetes mellitus and a main global cause of vision impairment. The manual diagnosis of diabetic retinopathy by ophthalmologists requires a significant amount of time, causing inconvenience and discomfort for patients. However, the use of automated technology makes it possible to quickly identify diabetic retinopathy, permitting the continuation of therapy without interruption and averting future ocular damage. This paper presents a comprehensive comparative analysis of six Convolutional Neural Networks and Deep Neural Networks based machine learning models, including simple CNN, VGG16, DenseNet121, ResNet50, InceptionV3, and EfficientNetB3, for the recognition of diabetic retinopathy using fundus photographs. The accuracy of various models is evaluated using the Cohen Kappa metric. The results of this study add a contribution to the research on the application of machine learning models for diagnosing diabetic retinopathy.

Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-aided diagnosis of diabetic retinopathy.

HIUNET: A Hybrid Inception U-Net for Diagnosis of Diabetic Retinopathy

Type 2 diabetes patients often suffer from microvascular complications of diabetes. These complications, in turn, often lead to vision impairment. Diabetic Retinopathy (DR) detection in its early stage can rescue people from long-term complications that could lead to permanent blindness. In this study, we propose a complex deep convolutional neural network architecture with an inception module for automated diagnosis of DR. The proposed novel Hybrid Inception U-Net (HIUNET) comprises various inception modules connected in the U-Net fashion using activation maximization and filter map to produce the image mask. First, inception blocks were used to enlarge the model’s width by substituting it with primary convolutional layers. Then, aggregation blocks were used to deepen the model to extract more compact and discriminating features. Finally, the downsampling blocks were adopted to reduce the feature map size to decrease the learning time, and the upsampling blocks were used to resize the feature maps. This methodology ensured high prominence to lesion regions compared to the non-lesion regions. The performance of the proposed model was assessed on two benchmark competitive datasets called Asia Pacific Tele-Ophthalmology Society (APTOS) and KAGGLE, attaining accuracy rates of 95% and 92%, respectively.