Detection of Diabetic Retinopathy from Retinal Fundus Image using Wavelet based Image Segmentation

Diabetic retinopathy is considered to be one of the major causes of blindness among diabetes mellitus patients. Due to diabetic retinopathy blood vessels of retina gets damaged and fat, lipoprotein substances gets leaked out of the damaged blood vessels and are deposited in the intra retinal space. These substances are viewed as yellowish or whitish in color and are termed as exudates. They are the most important visible sign of the presence of diabetic retinopathy. Exudates are of two types, (a) hard exudates and (b) soft exudates. If the disease is not detected in early stages then it may lead to complete loss of vision to the diabetes patients. Detection of exudates is extremely difficult to detect by visual inspection due to small inner diameter of retina and inadequate lighting conditions. An efficient image analysis program can detect the presence effectively. In this paper we have proposed an automatic method for detection of hard exudates. The proposed method exhibits a sensitivity of 97.60% and specificity of 93% and accuracy of 95.70%.

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Diabetic Retinopathy Detection using Image Processing

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f1179.0886s19 ◽

2019 ◽

Vol 8 (6S) ◽

pp. 937-941

Keyword(s):

Image Processing ◽

Diabetic Retinopathy ◽

Medical Image ◽

Histogram Equalization ◽

Fundus Image ◽

Adaptive Histogram Equalization ◽

Retinal Fundus Image ◽

Equalization Algorithm ◽

Processing Techniques ◽

Retinal Fundus

The main objective of this method is to detect DR (Diabetic Retinopathy) eye disease using Image Processing techniques. The tool used in this method is MATLAB (R2010a) and it is widely used in image processing. This paper proposes a method for Extraction of Blood Vessels from the medical image of human eye-retinal fundus image that can be used in ophthalmology for detecting DR. This method utilizes an approach of Adaptive Histogram Equalization using CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm with open CV (Computer Vision) framework implementation. The result shows that affected DR is detected in fundus image and the DR is not detected in the healthy fundus image and 98% of Accuracy can be achieved in the detection of DR.

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Diabetic Retinopathy Prediction Device System

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2019.8511 ◽

2019 ◽

Vol 16 (10) ◽

pp. 4266-4270

Author(s):

Meenu Garg ◽

Sheifali Gupta ◽

Rakesh Ahuja ◽

Deepali Gupta

Keyword(s):

Diabetic Retinopathy ◽

Deep Learning ◽

Treatment Effect ◽

Physiological Parameters ◽

Early Prediction ◽

Fundus Image ◽

Retinal Fundus Image ◽

Image Capturing ◽

The One ◽

Retinal Fundus

The present study relates to diagnostic devices, and more specifically, to a diabetic retinopathy prediction device, system and method for early prediction of diabetic retinopathy with application of deep learning. The device includes an image capturing device, a memory coupled to processor. The image capturing device obtains a retinal fundus image from the user. The memory comprising executable instructions which upon execution by the processor configures the device to obtain physiological parameters of the user in real-time from the image capturing device, retrieve the obtained retinal fundus image and the one or more obtained physiological parameters and compare the one or more extracted features with at least one pre-stored feature in a database to generate at least a prediction result indicative of detection of the presence, the progression or the treatment effect of the disease in the user.

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Red Lesion Detection in Retinal Fundus Image for Diagnosis of Diabetic Retinopathy

ICT Analysis and Applications - Lecture Notes in Networks and Systems ◽

10.1007/978-981-15-8354-4_33 ◽

2020 ◽

pp. 327-337

Author(s):

M. L. Jadhav ◽

M. Z. Shaikh ◽

V. M. Sardar

Keyword(s):

Diabetic Retinopathy ◽

Lesion Detection ◽

Fundus Image ◽

Retinal Fundus Image ◽

Retinal Fundus

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Pengaruh Preprocessing Terhadap Klasifikasi Diabetic Retinopathy dengan Pendekatan Transfer Learning Convolutional Neural Network

Jurnal Teknik Informatika dan Sistem Informasi ◽

10.28932/jutisi.v7i1.3327 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Juan Elisha Widyaya ◽

Setia Budi

Keyword(s):

Neural Network ◽

Diabetic Retinopathy ◽

Convolutional Neural Network ◽

Early Stage ◽

Careful Examination ◽

Fundus Images ◽

Fundus Image ◽

Retinal Fundus Image ◽

Retinal Fundus Images ◽

Retinal Fundus

Diabetic retinopathy (DR) is eye diseases caused by diabetic mellitus or sugar diseases. If DR is detected in early stage, the blindness that follow can be prevented. Ophthalmologist or eye clinician usually decide the stage of DR from retinal fundus images. Careful examination of retinal fundus images is time consuming task and require experienced clinicians or ophthalmologist but a computer which has been trained to recognize the DR stages can diagnose and give result in real-time manner. One approach of algorithm to train a computer to recognize an image is deep learning Convolutional Neural Network (CNN). CNN allows a computer to learn the features of an image, in our case is retinal fundus image, automatically. Preprocessing is usually done before a CNN model is trained. In this study, four preprocessing were carried out. Of the four preprocessing tested, preprocessing with CLAHE and unsharp masking on the green channel of the retinal fundus image give the best results with an accuracy of 79.79%, 82.97% precision, 74.64% recall, and 95.81% AUC. The CNN architecture used is Inception v3.

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Performance of Image Enhancement Methods for Diabetic Retinopathy based on Retinal Fundus Image

2020 IEEE 10th Symposium on Computer Applications & Industrial Electronics (ISCAIE) ◽

10.1109/iscaie47305.2020.9108707 ◽

2020 ◽

Author(s):

Nurul Atikah Binti Mohd Sharif ◽

Nor Hazlyna Binti Harun ◽

Yuhanis Binti Yusof ◽

Zunaina Embong ◽

Juhaida Binti Abu Bakar ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Image Enhancement ◽

Fundus Image ◽

Retinal Fundus Image ◽

Retinal Fundus

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Two-Tier Grading System for NPDR Severities of Diabetic Retinopathy in Retinal Fundus Images

Recent Patents on Engineering ◽

10.2174/1872212114666200109103922 ◽

2020 ◽

Vol 14 ◽

Author(s):

Charu Bhardwaj ◽

Shruti Jain ◽

Meenakshi Sood

Keyword(s):

Diabetic Retinopathy ◽

Statistical Analysis ◽

Early Stage ◽

Image Feature ◽

Grading System ◽

Fundus Images ◽

Fundus Image ◽

Retinal Fundus Images ◽

Retinal Fundus ◽

Timely Treatment

: Diabetic Retinopathy is the leading cause of vision impairment and its early stage diagnosis relies on regular monitoring and timely treatment for anomalies exhibiting subtle distinction among different severity grades. The existing Diabetic Retinopathy (DR) detection approaches are subjective, laborious and time consuming which can only be carried out by skilled professionals. All the patents related to DR detection and diagnoses applicable for our research problem were revised by the authors. The major limitation in classification of severities lies in poor discrimination between actual lesions, background noise and other anatomical structures. A robust and computationally efficient Two-Tier DR (2TDR) grading system is proposed in this paper to categorize various DR severities (mild, moderate and severe) present in retinal fundus images. In the proposed 2TDR grading system, input fundus image is subjected to background segmentation and the foreground fundus image is used for anomaly identification followed by GLCM feature extraction forming an image feature set. The novelty of our model lies in the exhaustive statistical analysis of extracted feature set to obtain optimal reduced image feature set employed further for classification. Classification outcomes are obtained for both extracted as well as reduced feature set to validate the significance of statistical analysis in severity classification and grading. For single tier classification stage, the proposed system achieves an overall accuracy of 100% by k- Nearest Neighbour (kNN) and Artificial Neural Network (ANN) classifier. In second tier classification stage an overall accuracy of 95.3% with kNN and 98.0% with ANN is achieved for all stages utilizing optimal reduced feature set. 2TDR system demonstrates overall improvement in classification performance by 2% and 6% for kNN and ANN respectively after feature set reduction, and also outperforms the accuracy obtained by other state of the art methods when applied to the MESSIDOR dataset. This application oriented work aids in accurate DR classification for effective diagnosis and timely treatment of severe retinal ailment.

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