Diabetic Retinopathy Detection by Extracting Area and Number of Microaneurysm from Colour Fundus Image

Kumar, Shailesh; Kumar, Basant

doi:10.1109/spin.2018.8474264

Cited by 71 publications

(17 citation statements)

References 9 publications

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“…Kumar et al [5] classified 89 images from DIARETDB1 dataset into two stages after removing the noise on the images by using (CALHE) histogram equalization. They also extracted hard exudates, Blood vessels, the area of MA, and the number of MA.…”

Section: A Svm Classifiermentioning

confidence: 99%

Automatic Classification of Preliminary Diabetic Retinopathy Stages using CNN

Khaled¹,

ElSahhar²,

El-Dine³

et al. 2021

IJACSA

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Diabetes Mellitus is one of the modern world's most prominent and dominant maladies. This condition later on leads to a menacing eye disease called Diabetic Retinopathy (DR). Diabetic Retinopathy is a retinal disease that is caused by high blood sugar levels in the retina, and can naturally progress to irreversible vision loss (blindness). The primary purpose of this imperative research is the early detection and classification of this hazardous condition, to try and prevent any threatening complications in the future. In the course of recent years, Convolutional Neural Networks (CNNs) turned out to be exceptionally famous and fruitful in solving and unraveling image processing and object detection problems for enormous datasets. Throughout this pivotal research, a model was proposed to detect the presence of (DR) and classify it into 5 distinct stages, factoring in an immense and substantial dataset. The model starts by applying preprocessing techniques such as normalization, to maintain the same dimensions for all the images before proceeding to the main processing stage. Furthermore, diverse sampling methods such as "Resize & Crop", "Rotation", and "Flipping" have been tested out, so as to pinpoint the best augmentation technique. Finally, the normalized images were fed into a Convolutional Neural Network (CNN), to predict whether a person suffers from DR or not, and classify the level/stage of the disease. The proposed method was utilized on 88,700 retinal fundus images, which are a parcel of the full (EyePACS) dataset, and finally achieved 81.12%, 89.16%, and 84.16% for sensitivity, specificity, and accuracy, respectively.

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Section: A Svm Classifiermentioning

confidence: 99%

Automatic Classification of Preliminary Diabetic Retinopathy Stages using CNN

Khaled¹,

ElSahhar²,

El-Dine³

et al. 2021

IJACSA

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“…To estimate the performance of our model in multiclass classification tasks we use metrics like accuracy, sensitivity and specificity [16][17].…”

Section: Statistical Analysis Of Performancementioning

confidence: 99%

Fine Tuned VGG19 Convolutional Neural Network Architecture for Diabetic Retinopathy Diagnosis

Vijayan¹,

Sangeetha²,

Kumaravel³

et al. 2020

INDJCSE

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Diabetic Retinopathy (DR) is an eye-related complication experienced by individuals with longstanding diabetes. Usually diagnosed by the healthcare professional by retinal fundus examination during Medical check-ups or mass screenings. Early detection of diabetic retinopathy will avoid vision loss and other issues. The objective of this work is to diagnose the Diabetic Retinopathy from retinal fundus images using deep learning (DL) techniques for better detection accuracy.The proposed fine-tunedVGG19 CNN architecture is performing well with the Kaggle data set and effectively dealing with this multi-class classification problem. The proposed model uses pre-trained weights from image net data which lessens the training time and improves the performance in detecting DR from retinal fundus images in terms of sensitivity, specificity, and accuracy. Deep transfer learning with fine-tuning method implementation was carried out to get the highest test accuracy of 73.60%.

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“…Microaneurysms are localized outpouchings formed by focal dilatation of capillary walls. At initial stages, recognition of Microaneurysms (MA) is very crucial and it can be considered as the first step in inhibiting diabetic retinopathy [4].…”

Section: A Diabetic Retinopathymentioning

confidence: 99%

Retinal disorder as a biomarker for detection of human diseases

Malik

Batool

Rubab

et al. 2020

2020 IEEE 23rd International Multitopic Conference (INMIC)

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The rapid development of digital imaging and computer vision has extended the potential of using these technologies in the diagnosis of human diseases. Medical image analysis is a non-invasive way to identify human diseases which are usually manually marked by medical experts or automatically detected by the Computer-Aided Diagnostic (CAD) system. The fundus images provide facts and information about visual disorders (diabetic retinopathy, glaucoma, age-related macular degeneration, etc.), brain disease (Alzheimer's), and heart-related diseases (cardiovascular, hypertension, and stroke). Extensive research has been done in the last two decades in developing automated methods for the identification of various human disorders from fundus images. This survey paper provides the reader with a broad review of the fundus image processing for the identification of various human disorders. In this paper, human diseases that can be diagnosed through fundus images are reviewed. Several publicly available fundus datasets which are helpful in the screening of these abnormalities are briefly discussed. This work aims to show the importance of retinal images in the diagnosis of different human diseases. The main challenges and difficulties faced in effective diagnosis are presented and discussed. We believe that this work will guide the researchers in addressing the challenges in existing solutions and assist doctors in the proper diagnosis of various diseases.

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Diabetic Retinopathy Detection by Extracting Area and Number of Microaneurysm from Colour Fundus Image

Cited by 71 publications

References 9 publications

Automatic Classification of Preliminary Diabetic Retinopathy Stages using CNN

Automatic Classification of Preliminary Diabetic Retinopathy Stages using CNN

Fine Tuned VGG19 Convolutional Neural Network Architecture for Diabetic Retinopathy Diagnosis

Retinal disorder as a biomarker for detection of human diseases

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