Segmentation of Microaneurysms for Early Detection of Diabetic Retinopathy Using MResUNet

Qomariah, Dinial Utami Nurul; Tjandrasa, Handayani; Fatichah, Chastine

doi:10.22266/ijies2021.0630.30

Cited by 12 publications

(8 citation statements)

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“…Secondly, the classification algorithm is applied to the classification of these features for the candidates of MA and the candidate of non-MA [17]. In recent works [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34], the same procedure has been applied by employing different machine learning, deep learning, and image processing methods. For example, in [35], a deep learning method based on transformation splicing (TS) and multicontext ensemble learning is proposed.…”

Section: Fundusmentioning

confidence: 99%

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Automatic Detection of Microaneurysms in OCT Images Using Bag of Features

Kazeminasab

Almasi

Shoushtarian

et al. 2022

Computational and Mathematical Methods in Medicine

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Diabetic retinopathy (DR) caused by diabetes occurs as a result of changes in the retinal vessels and causes visual impairment. Microaneurysms (MAs) are the early clinical signs of DR, whose timely diagnosis can help detecting DR in the early stages of its development. It has been observed that MAs are more common in the inner retinal layers compared to the outer retinal layers in eyes suffering from DR. Optical coherence tomography (OCT) is a noninvasive imaging technique that provides a cross-sectional view of the retina, and it has been used in recent years to diagnose many eye diseases. As a result, this paper attempts to identify areas with MA from normal areas of the retina using OCT images. This work is done using the dataset collected from FA and OCT images of 20 patients with DR. In this regard, firstly fluorescein angiography (FA) and OCT images were registered. Then, the MA and normal areas were separated, and the features of each of these areas were extracted using the Bag of Features (BOF) approach with the Speeded-Up Robust Feature (SURF) descriptor. Finally, the classification process was performed using a multilayer perceptron network. For each of the criteria of accuracy, sensitivity, specificity, and precision, the obtained results were 96.33%, 97.33%, 95.4%, and 95.28%, respectively. Utilizing OCT images to detect MAs automatically is a new idea, and the results obtained as preliminary research in this field are promising.

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Section: Fundusmentioning

confidence: 99%

“…Computational and Mathematical Methods in Medicine of their very small size [32], interference with blood vessels, and apparent contrast differences between MAs [35]. Also, the major limitation of these images is that they provide a two-dimensional view of the 3D retinal texture [40].…”

Section: Fundusmentioning

confidence: 99%

Automatic Detection of Microaneurysms in OCT Images Using Bag of Features

Kazeminasab

Almasi

Shoushtarian

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Shortcuts are used in conjunction with FCN-8 units, with each unit being derived in Eqs. ( 6) and (7).…”

Section: Shortcut/identity Mappingmentioning

confidence: 99%

“…Diabetic retinopathy is suffered by many productive societies around the world [4,5]. Diabetic retinopathy is characterized by vascular abnormalities in the retina [6,7]. The severity of diabetic retinopathy is divided into two, namely nonproliferative and proliferative diabetic retinopathy [8,9].…”

Section: Introductionmentioning

confidence: 99%

Exudate Segmentation for Diabetic Retinopathy Using Modified FCN-8 and Dice Loss

2022

IJIES

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The severity of diabetic retinopathy can lead to blindness if undiagnosed and untreated. The presence of hard exudates is one of the diabetic retinopathy symptoms. Therefore, automatic segmentation of hard exudates can provide an important diagnosis for diabetic retinopathy. Due to the relatively small dimensions of the exudates and the availability of the optic disc that has similar color, the exudate segmentation is a challenge in itself. In this study, we propose a modification of the fully convolutional network model (FCN-8) by combining FCN-8 and shortcuts to improve the performance of FCN-8. Each shortcut consists of a convolutional layer and batch normalization to reduce input degradation. Prior to processing the hard exudates using a modified FCN-8, the optic disc was removed from the retinal image by detecting the area using Faster R-CNN based on the Alexnet architecture. For training and testing, we applied the IDRiD dataset to evaluate the performance of our proposed architecture. Experiments show that our proposed architecture provides accuracy, sensitivity, specificity of 98.18 %, 81.7%, and 98.37 % respectively. Our proposed method gives higher sensitivity compared to Autoencoder, U-Net,

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“…In [12], the authors used a multi-model to segment MRI tumor images based on ML, DL and conventional methods. Moreover, in [13], the authors modify the basic U-net model using residual and identity mapping for the segmentation of microaneurysms. The Harris corner finder was utilized to find the OD center as well as accomplish 97.8% for the nearby dataset, 97.5% for the DRIVE and STARE datasets, as well as 86.75% for the DRIVE and STARE datasets, separately [14].…”

Section: Introductionmentioning

confidence: 99%

AI-Based Automatic Detection and Classification of Diabetic Retinopathy Using U-Net and Deep Learning

et al. 2022

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Artificial intelligence is widely applied to automate Diabetic retinopathy diagnosis. Diabetes-related retinal vascular disease is one of the world’s most common leading causes of blindness and vision impairment. Therefore, automated DR detection systems would greatly benefit the early screening and treatment of DR and prevent vision loss caused by it. Researchers have proposed several systems to detect abnormalities in retinal images in the past few years. However, Diabetic Retinopathy automatic detection methods have traditionally been based on hand-crafted feature extraction from the retinal images and using a classifier to obtain the final classification. DNN (Deep neural networks) have made several changes in the previous few years to assist overcome the problem mentioned above. We suggested a two-stage novel approach for automated DR classification in this research. Due to the low fraction of positive instances in the asymmetric Optic Disk (OD) and blood vessels (BV) detection system, preprocessing and data augmentation techniques are used to enhance the image quality and quantity. The first step uses two independent U-Net models for OD (optic disc) and BV (blood vessel) segmentation. In the second stage, the symmetric hybrid CNN-SVD model was created after preprocessing to extract and choose the most discriminant features following OD and BV extraction using Inception-V3 based on transfer learning, and detects DR by recognizing retinal biomarkers such as MA (microaneurysms), HM (hemorrhages), and exudates (EX). On EyePACS-1, Messidor-2, and DIARETDB0, the proposed methodology demonstrated state-of-the-art performance, with an average accuracy of 97.92%, 94.59%, and 93.52%, respectively. Extensive testing and comparisons with baseline approaches indicate the efficacy of the suggested methodology.

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Segmentation of Microaneurysms for Early Detection of Diabetic Retinopathy Using MResUNet

Cited by 12 publications

References 0 publications

Automatic Detection of Microaneurysms in OCT Images Using Bag of Features

Automatic Detection of Microaneurysms in OCT Images Using Bag of Features

Exudate Segmentation for Diabetic Retinopathy Using Modified FCN-8 and Dice Loss

AI-Based Automatic Detection and Classification of Diabetic Retinopathy Using U-Net and Deep Learning

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