Detection Of Microaneurysms Research Articles

Deep Learning AI for DR Microaneurysm Screening Based on Fundus Images

Abstract. Since 2014, Artificial Intelligence (AI) image detection has developed rapidly in the medical field, while the global incidence of diabetes has increased rapidly. As an early sign of diabetic retinopathy (DR), the detection of diabetic microaneurysms is particularly important for the prevention and treatment of diabetes. Therefore, the artificial intelligence exhibition of DR microaneurysm screening based on fundus images shows great potential in this regard. This paper reviews the research progress of artificial intelligence technology for DR microangioma screening based on fundus images in recent years, and focuses on the improvement measures taken for different depth learning methods. These improvement measures aim to optimize model performance, improve training efficiency and enhance the generalization ability of the model. Some model experimental results are compared and analyzed in order to get the best model in a specific dataset or result. Finally, the current challenges are discussed and suggestions for the development of this field are put forward.

Hybrid convolutional neural network for detection of microaneurysms and exudates in retinal images

Hybrid convolutional neural network for detection of microaneurysms and exudates in retinal images

Ensembling U-Nets for microaneurysm segmentation in optical coherence tomography angiography in patients with diabetic retinopathy

Diabetic retinopathy is one of the leading causes of blindness around the world. This makes early diagnosis and treatment important in preventing vision loss in a large number of patients. Microaneurysms are the key hallmark of the early stage of the disease, non-proliferative diabetic retinopathy, and can be detected using OCT angiography quickly and non-invasively. Screening tools for non-proliferative diabetic retinopathy using OCT angiography thus have the potential to lead to improved outcomes in patients. We compared different configurations of ensembled U-nets to automatically segment microaneurysms from OCT angiography fundus projections. For this purpose, we created a new database to train and evaluate the U-nets, created by two expert graders in two stages of grading. We present the first U-net neural networks using ensembling for the detection of microaneurysms from OCT angiography en face images from the superficial and deep capillary plexuses in patients with non-proliferative diabetic retinopathy trained on a database labeled by two experts with repeats.

Automatic detection of microaneurysms using DeTraC deep convolutional neural network classifier with woodpecker mating algorithm

ABSTRACT Diabetic Retinopathy (DR) is a microvascular complication of diabetes that leads to visual blindness. Early identification of DR can prevent the loss of sight. The first visible sign of DR is the appearance of micro aneurysms (MAs). Micro aneurysms are seen as small red circular spots on the retinal surface. The very small size of micro aneurysms proves to be challenging in its proper detection. In this research, DeTrac Deep Convolutional Neural Network m classifier with Woodpecker Mating Algorithm is proposed for the detection of MAs. By using this technique, every image is classified as either MAs or non-MAs pixel to automatically detect micro aneurysms from the retinal images. Experimental results are evaluated on diabetic-retinopathy-detection (DRD) dataset of the Kaggle website. Extensive simulations on this dataset shows an improved performance over the existing methods with 0.98 mean sensitivity, 0.97 mean specificity, and 0.98 mean accuracy in detecting the MAs irrespective of their intrinsic properties.

Segmentation of retinal microaneurysms in fluorescein fundus angiography images by a novel three-step model.

Microaneurysms serve as early signs of diabetic retinopathy, and their accurate detection is critical for effective treatment. Due to their low contrast and similarity to retinal vessels, distinguishing microaneurysms from background noise and retinal vessels in fluorescein fundus angiography (FFA) images poses a significant challenge. We present a model for automatic detection of microaneurysms. FFA images were pre-processed using Top-hat transformation, Gray-stretching, and Gaussian filter techniques to eliminate noise. The candidate microaneurysms were coarsely segmented using an improved matched filter algorithm. Real microaneurysms were segmented by a morphological strategy. To evaluate the segmentation performance, our proposed model was compared against other models, including Otsu's method, Region Growing, Global Threshold, Matched Filter, Fuzzy c-means, and K-means, using both self-constructed and publicly available datasets. Performance metrics such as accuracy, sensitivity, specificity, positive predictive value, and intersection-over-union were calculated. The proposed model outperforms other models in terms of accuracy, sensitivity, specificity, positive predictive value, and intersection-over-union. The segmentation results obtained with our model closely align with benchmark standard. Our model demonstrates significant advantages for microaneurysm segmentation in FFA images and holds promise for clinical application in the diagnosis of diabetic retinopathy. The proposed model offers a robust and accurate approach to microaneurysm detection, outperforming existing methods and demonstrating potential for clinical application in the effective treatment of diabetic retinopathy.

Correction to: Advances in retinal microaneurysms detection, segmentation and datasets for the diagnosis of diabetic retinopathy: a systematic literature review

Correction to: Advances in retinal microaneurysms detection, segmentation and datasets for the diagnosis of diabetic retinopathy: a systematic literature review

GravityNet for end-to-end small lesion detection

This paper introduces a novel one-stage end-to-end detector specifically designed to detect small lesions in medical images. Precise localization of small lesions presents challenges due to their appearance and the diverse contextual backgrounds in which they are found. To address this, our approach introduces a new type of pixel-based anchor that dynamically moves towards the targeted lesion for detection. We refer to this new architecture as GravityNet, and the novel anchors as gravity points since they appear to be “attracted” by the lesions. We conducted experiments on two well-established medical problems involving small lesions to evaluate the performance of the proposed approach: microcalcifications detection in digital mammograms and microaneurysms detection in digital fundus images. Our method demonstrates promising results in effectively detecting small lesions in these medical imaging tasks.

Resilient back-propagation machine learning-based classification on fundus images for retinal microaneurysm detection.

The timely diagnosis of medical conditions, particularly diabetic retinopathy, relies on the identification of retinal microaneurysms. However, the commonly used retinography method poses a challenge due to the diminutive dimensions and limited differentiation of microaneurysms in images. Automated identification of microaneurysms becomes crucial, necessitating the use of comprehensive ad-hoc processing techniques. Although fluorescein angiography enhances detectability, its invasiveness limits its suitability for routine preventative screening. This study proposes a novel approach for detecting retinal microaneurysms using a fundus scan, leveraging circular reference-based shape features (CR-SF) and radial gradient-based texture features (RG-TF). The proposed technique involves extracting CR-SF and RG-TF for each candidate microaneurysm, employing a robust back-propagation machine learning method for training. During testing, extracted features from test images are compared with training features to categorize microaneurysm presence. The experimental assessment utilized four datasets (MESSIDOR, Diaretdb1, e-ophtha-MA, and ROC), employing various measures. The proposed approach demonstrated high accuracy (98.01%), sensitivity (98.74%), specificity (97.12%), and area under the curve (91.72%). The presented approach showcases a successful method for detecting retinal microaneurysms using a fundus scan, providing promising accuracy and sensitivity. This non-invasive technique holds potential for effective screening in diabetic retinopathy and other related medical conditions.

Advances in retinal microaneurysms detection, segmentation and datasets for the diagnosis of diabetic retinopathy: a systematic literature review

Advances in retinal microaneurysms detection, segmentation and datasets for the diagnosis of diabetic retinopathy: a systematic literature review

Transforming Diabetic Retinopathy Detection: Advanced Strategies with Convolutional Neural Networks

Diabetic retinopathy is an aspect of diabetes which produces vascular abnormalities that might result in blindness. Since the symptoms with this illness are lasting, detection at an early stage is crucial because untreated eye disease may lead to blindness. The detection of microaneurysms using digital color fundus images is an essential initial step in automated diabetic retinopathy testing. Five different DR stages or classes (proliferative diabetic retinopathy), typical, mild, moderate, serious, and PDR, are typically assessed by expert doctors to determine this disastrous disorder. Manually diagnosis (by physicians) for such ailment requires time and is vulnerable to inaccuracy. There have been several computer vision-based algorithms created for autonomously recognizing DR and its different stages from retina scans. The results of this research utilize fundus-colored images to provide a computational method for recognizing and categorizing the condition via a convolution neural network.

Biomarkers in diabetic retinopathy

Diabetic retinopathy (DR) is a leading cause of blindness in working‐age adults worldwide, affecting up to 80% of patients who have had diabetes for more than 10 years. Biomarkers are measurable indicators that can be used to diagnose diseases or monitor their progression. In DR, biomarkers can be found in various structures of the eye, including the retina, vitreous, and aqueous humour.One of the most commonly used biomarkers in DR is glycated haemoglobin (HbA1c), which reflects long‐term blood glucose control. Elevated HbA1c levels are associated with an increased risk of developing DR, and strict glycemic control can help to prevent or delay its onset.Other biomarkers in DR include imaging techniques such as optical coherence tomography (OCT) and fundus photography, which can provide detailed images of the retina and allow for the detection of microaneurysms, haemorrhages, and exudates. OCT angiography (OCTA) can also visualize changes in retinal blood flow, which may be indicative of early DR.In addition to structural biomarkers, there are also functional biomarkers that can be used to evaluate visual function, such as contrast sensitivity and visual field testing. These measures are often used to monitor the progression of DR and assess the efficacy of treatment.Recent advances in biomarker research have led to the discovery of new markers for DR, including genetic markers, inflammatory markers, and metabolic markers. Genetic markers, such as single nucleotide polymorphisms (SNPs), have been associated with an increased risk of developing DR. Inflammatory markers, such as cytokines and chemokines, are elevated in the aqueous humour and vitreous of patients with DR and may contribute to the development and progression of the disease. Metabolic markers, such as advanced glycation end‐products (AGEs), are elevated in patients with DR and may play a role in the pathogenesis of the disease.Overall, biomarkers are an important tool in the diagnosis, monitoring, and management of DR. They may also help to improve our understanding of the disease and facilitate the development of new therapies. By identifying patients at high risk of developing DR, biomarkers may enable earlier interventions to prevent or delay the onset of vision loss.

Microaneurysm detection using high-speed megahertz optical coherence tomography angiography in advanced diabetic retinopathy.

To compare detection rates of microaneurysms (MAs) on high-speed megahertz optical coherence tomography angiography (MHz-OCTA), fluorescein angiography (FA) and colour fundus photography (CF) in patients with diabetic retinopathy (DR). For this exploratory cross-sectional study, MHz-OCTA data were acquired with a swept-source OCT prototype (A-scan rate: 1.7 MHz), and FA and CF imaging was performed using Optos® California. MA count was manually evaluated on en face MHz-OCTA/FA/CF images within an extended ETDRS grid. Detectability of MAs visible on FA images was evaluated on corresponding MHz-OCTA and CF images. MA distribution and leakage were correlated with detectability on OCTA and CF imaging. 47 eyes with severe DR (n = 12) and proliferative DR (n = 35) were included. MHz-OCTA and CF imaging detected on average 56% and 36% of MAs, respectively. MHz-OCTA detection rate was significantly higher than CF (p < 0.01). The combination of MHz-OCTA and CF leads to an increased detection rate of 70%. There was no statistically significant association between leakage and MA detectability on OCTA (p = 0.13). For CF, the odds of detecting leaking MAs were significantly lower than non-leaking MAs (p = 0.012). Using MHz-OCTA, detection of MAs outside the ETDRS grid was less likely than MAs located within the ETDRS grid (outer ring, p < 0.01; inner ring, p = 0.028). No statistically significant difference between rings was observed for CF measurements. More MAs were detected on MHz-OCTA than on CF imaging. Detection rate was lower for MAs located outside the macular region with MHz-OCTA and for leaking MAs with CF imaging. Combining both non-invasive modalities can improve MA detection.

An Improved Microaneurysm Detection Model Based on SwinIR and YOLOv8.

Diabetic retinopathy (DR) is a microvascular complication of diabetes. Microaneurysms (MAs) are often observed in the retinal vessels of diabetic patients and represent one of the earliest signs of DR. Accurate and efficient detection of MAs is crucial for the diagnosis of DR. In this study, an automatic model (MA-YOLO) is proposed for MA detection in fluorescein angiography (FFA) images. To obtain detailed features and improve the discriminability of MAs in FFA images, SwinIR was utilized to reconstruct super-resolution images. To solve the problems of missed detection of small features and feature information loss, an MA detection layer was added between the neck and the head sections of YOLOv8. To enhance the generalization ability of the MA-YOLO model, transfer learning was conducted between high-resolution images and low-resolution images. To avoid excessive penalization due to geometric factors and address sample distribution imbalance, the loss function was optimized by taking the Wise-IoU loss as a bounding box regression loss. The performance of the MA-YOLO model in MA detection was compared with that of other state-of-the-art models, including SSD, RetinaNet, YOLOv5, YOLOX, and YOLOv7. The results showed that the MA-YOLO model had the best performance in MA detection, as shown by its optimal metrics, including recall, precision, F1 score, and AP, which were 88.23%, 97.98%, 92.85%, and 94.62%, respectively. Collectively, the proposed MA-YOLO model is suitable for the automatic detection of MAs in FFA images, which can assist ophthalmologists in the diagnosis of the progression of DR.

Detection of microaneurysms for diabetic patients using ensemble method

Detection of microaneurysms for diabetic patients using ensemble method

Microaneurysms detection using fundus images based on deep convolutional neural network enabled fractional hunter osprey optimization

Microaneurysms detection using fundus images based on deep convolutional neural network enabled fractional hunter osprey optimization

Automated microaneurysms detection in retinal images using SSA optimised U-NET and Bayesian optimised CNN

ABSTRACT Diabetic retinopathy (DR) is a cause of vision loss and blindness, and early detection of red lesions, particularly microaneurysms (MAs), is crucial to prevent its progression. In this study, we propose an effective deep learning-based DR segmentation and classification method that leverages hyper-parameter optimisation to achieve high detection accuracy. In our proposed method, we first pre-processed retinal images to reduce background variability. Then, we employed a squirrel search optimised bidirectional ConvLSTM U-net with an attention gate for DR segmentation in the main processing step. Finally, we used a Bayesian-optimised Convolutional Neural Network for classification. We selected the optimal architectures for our proposed CNNs and optimised their hyper-parameters using the Bayesian optimisation technique.Our study demonstrates that the proposed CNNs effectively classify diabetic retinopathy in fundus images when model hyper-parameters are tuned with the help of Bayesian optimisation. We conducted statistical tests based on the receiver operating characteristic curve, histogram, and ANOVA test to verify the effectiveness of our suggested models.We proposed a deep learning-based DR segmentation and classification method that leverages hyper-parameter optimisation to achieve high detection accuracy. This study highlights the potential of Bayesian optimisation as an effective technique to optimise hyper-parameters of deep learning networks for disease classification tasks.

Microaneurysms detection in fundus images using local Fourier transform and neighbourhood analysis

Microaneurysms detection in fundus images using local Fourier transform and neighbourhood analysis

Optimizing Microaneurysm Detection for Early Diagnosis of Diabetic Retinopathy with Enhanced Fundus Images

Diabetic Retinopathy (DR) stands as the primary cause of visual impairment among individuals in the workforce. Detecting it early through routine screenings and providing accurate treatment has proven effective in preventing vision loss. Fundus photography enables the capture of detailed images of the eye's interior. The automated identification of lesions offers a straightforward and economical approach. This technology aids doctors in decreasing their workload while enhancing accuracy and efficiency. The focus of our research revolves around identifying red lesions, characterized by minuscule dots in fundus images. These dots signify the presence of microaneurysms, which represent an initial stage in diabetic retinopathy detection. Given that microaneurysms serve as symptomatic indicators of diabetic retinopathy, their identification holds crucial importance. Our approach is built upon a highly efficient level set framework, designed to effectively detect the presence of these lesions within the affected images.

Detection and location of microaneurysms in fundus images based on improved YOLOv4 with IFCM

AbstractDiabetic retinopathy (DR) is one of the leading causes of blindness for people suffering from diabetes. Microaneurysm (MA) is the initial symptom of DR. MA is a lesion based disease which starts as small red spots on the retina and increases in size as the DR progresses which finally leads to blindness. So eliminating the lesion can effectively prevent DR at an early stage. However, due to complex retinal structure, different brightness and contrast of fundus images with effects of factors such as different patients, environment changes, and difference in acquisition equipment, it is difficult for existing detection algorithms to achieve accurate results of MA detection and location. Therefore, the detection algorithm of improved YOLOv4 (YOLOv4‐Pro) was proposed. First, an improved Fuzzy C‐Means (IFCM) clustering algorithm was proposed to optimize anchor parameters of target samples to improve matching results between anchors and feature graphs. In order to control noise and improve efficiency, a median filtering method was employed to update the criterion function of the original FCM algorithm, and a K‐means algorithm was employed to initialize clustering. Second, a SENet attention module was added in the backbone of YOLOv4 to enhance key information and suppress background, improving the confidence of MA effectively. Finally, the spatial pyramid pooling (SPP) module was added to the neck to enhance the acceptance domain of the output characteristics of the backbone network, and profits separating of important context information. The improved YOLOv4 with IFCM was verified on the Kaggle DR dataset and compared with other methods. Experimental results show that optimizing the prior frame with the IFCM algorithm can make it suitable to frame the Kaggle DR dataset, which improves the detection accuracy of the network by nearly 5%, and provides a nice performance on detection and location of MA in fundus images. This would help ophthalmologists finding the exact location of MA on retina, thereby simplifying the process and eliminating any manual intervention.

Automatic detection of microaneurysms using a novel segmentation algorithm based on deep learning techniques

AbstractMicroaneurysms is the first stage of diabetic retinopathy (DR) and it plays a vital role in the computerized diagnosis. However, it is difficult to automatically detect microaneurysms in fundus images due to the complicated background and various illumination reasons. The motivation behind this, is the number of increases in diabetic patients is very large when compared with the number of ophthalmologists. The FSCA‐UNET (Frequency Spatial Channel Attention UNET) segmentation model, is proposed and it is an improvement over UNET. We first use the frequency channel attention mechanism to analyze the features that were extracted from the first stage of the convolution layer, and we obtain good results. Then, we included a spatial attention map with frequency attention, also known as FSCA‐UNET, which makes use of inter‐spatial connections between features. Our deep neural model with an encoder‐decoder structure termed FSCA‐UNET produced more accurate results. Our novel algorithm outdated the performance measures of the existing segmentation algorithms. The proposed segmentation algorithm was trained and tested on Indian Diabetic Retinopathy Image Dataset (IDRiD), and E‐ophtha Dataset and we got promising results in terms of sensitivity, specificity, dice coefficient, precision, F1 score, and accuracy.