Computerized Diabetic Retinopathy Diagnosis with Optimized Random Forest

2019 ◽  
Vol 9 (2) ◽  
pp. 274-283
Author(s):  
Xiaobo Lai ◽  
LV Lili ◽  
Zihe Huang
Keyword(s):  
Diabetic Retinopathy ◽  
Random Forest ◽  
Proliferative Diabetic Retinopathy ◽  
Screening Program ◽  
Early Stage ◽  
Treatment Plan ◽  
Texture Features ◽  
Cost Effective ◽  
Fundus Images ◽  
Diabetic Retinopathy Screening

It is desirable to diagnose diabetic retinopathy at an early stage for developing a suitable treatment plan to prevent the condition from deteriorating. To provide an immediate diagnosis of the retina, various methods have been investigated to realize a time and cost effective classification of the fundus images. However, most diabetic retinopathy automated identification methods are structural based analysis. Moreover, Asian fundus images have larger optic disc and thicker retinal vessels compared with Caucasians. Hence, we explore a machine learning approach to the extraction of texture features for classification and the feasibility of this approach using texture parameters to complement current algorithms. Normal retina, non-proliferative diabetic retinopathy and proliferative diabetic retinopathy are identified in this paper. The first step is achieved with three groups of texture features such as gray level co-occurrence matric texture features, different statistical features and run length matrix texture features extracted. In the second step, these features are fed into an optimized random forest classifier for automatic classification. We test our system on two databases (D1 and D2) consisting of 432 and 579 fundus images from a diabetic retinopathy screening program consisting of Asians. The diabetic retinopathy is successfully diagnosed with sensitivity is 0.936 ± 0.019 for D1 and 0.941 ± 0.016 for D2, specificity is 0.917 ± 0.011 for D1 and 0.918 ± 0.011 for D2, positive predictive value is 0.924 ± 0.013 for D1 and 0.939 ± 0.012 for D2, when training on the same institutions, respectively.

Cost-Effectiveness Analysis of Non-Mydriatic Ultrawide- Field Fundus Photography versus Pharmacological Pupil Dilatation in Diabetic Retinopathy Screening Program

2021 ◽  
Vol 104 (5) ◽  
pp. 818-824
Keyword(s):  
Diabetic Retinopathy ◽  
Cost Effectiveness ◽  
Screening Program ◽  
Cost Effective ◽  
Cost Effectiveness Analysis ◽  
Fundus Photography ◽  
Diabetic Retinopathy Screening ◽  
Effectiveness Analysis ◽  
The Cost ◽  
Retinopathy Screening

Background: Diabetic retinopathy (DR) causes blindness of the population in many countries worldwide. Early detection and treatment of this disease via a DR screening program is the best way to secure the vision. An annual screening program using pharmacological pupil dilatation becomes the standard method. Recently, non-mydriatic ultrawide-field fundus photography (UWF) has been proposed as a choice for DR screening. However, there was no cost-effectiveness study between the standard DR screening and this UWF approach. Objective: To compare the cost-effectiveness between UWF and pharmacological pupil dilatation in terms of hospital and societal perspectives. Materials and Methods: Patients with type 2 diabetes mellitus that visited the ophthalmology clinic at Chulabhorn Hospital for DR screening were randomized using simple randomization method. The patients were interviewed by a trained interviewer for general and economic information. The clinical characteristics of DR and staging were recorded. Direct medical costs, direct non-medical costs, and informal care costs due to DR screening were recorded. Cost analyses were calculated for the hospital and societal perspectives. Results: The present study presented the cost-effectiveness analyses of UWF versus pharmacological pupil dilatation. Cost-effectiveness analysis from the hospital perspective showed the incremental cost-effectiveness ratio (ICER) of UWF to be –13.87. UWF was a cost-effective mean in DR screening in the societal perspective when compared with pharmacologically pupil dilatation with the ICER of 76.46, under the threshold of willingness to pay. Conclusion: The UWF was a cost-effective mean in DR screening. It can reduce screening duration and bypass post-screening blurred vision. The results suggested that UWF could be a viable option for DR screening. Keywords: Diabetic retinopathy, Diabetic retinopathy screening, Non-mydriatic ultrawide-field fundus photography, Cost-effectiveness analysis

scholarly journals Klasifikasi Fase Retinopati Diabetes Menggunakan Backpropagation Neural Network

2013 ◽  
Vol 7 (1) ◽  
pp. 23
Author(s):  
Rocky Yefrenes Dillak ◽  
Agus Harjoko
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Macular Edema ◽  
Proliferative Diabetic Retinopathy ◽  
Texture Features ◽  
Fundus Images ◽  
Gray Level ◽  
Backpropagation Neural Network ◽  
Maximum Probability ◽  
Keywords Diabetic Retinopathy

AbstrakRetinopati diabetes (DR) merupakan salah satu komplikasi pada retina yang disebabkan oleh penyakit diabetes. Tingkat keparahan DR dibagi atas empat kelas yakni: normal, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), dan macular edema (ME). Penelitian ini bertujuan mengembangkan suatu metode yang dapat digunakan untuk melakukan klasifikasi terhadap fase DR. Data yang digunakan sebanyak 97 citra yang fitur – fiturnya  diekstrak menggunakan gray level cooccurence matrix (GLCM). Fitur ciri tersebut adalah maximum probability, correlation, contrast, energy, homogeneity, dan entropy. Fitur – fitur ini dilatih menggunakan jaringan syaraf tiruan backpropagation untuk dilakukan klasifikasi. Kinerja  yang dihasilkan dari pendekatan ini adalah sensitivity 100%, specificity 100% dan accuracy  97.73%  Kata kunci— fase retinopati diabetes, GLCM, backpropagation neural network  Abstract Diabetic retinopathy (DR) is one of the complications on retina caused by diabetes. The aim of this studyis to develop a system that can be used for automatic mass screenings of diabetic retinopathy. Four classes are identified: normal retina, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and macular edema (ME). Ninenty-seven retinal fundus images in used in this study. Six different texture features such as maximum probability, correlation, contrast, energy, homogeneity, and entropy were extracted from the digital fundus images using gray level cooccurence matrix (GLCM). These features were fed into a backpropagation neural network classifier for automatic classification. The  proposed approach is able to classify with sensitivity 100%, specificity 100% and accuracy  97.73%  Keywords— diabetic retinopathy stages, GLCM,  backpropagation neural network

scholarly journals The Diagnostic Accuracy of an Intelligent and Automated Fundus Disease Image Assessment System with Lesion Quantitative Function (SmartEye) in Diabetic Patients

2019 ◽  
Author(s):  
Yi XU ◽  
Yongyi WANG ◽  
Bin LIU ◽  
Lin TANG ◽  
Liangqing LV ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Quantitative Analysis ◽  
Diagnostic Accuracy ◽  
Proliferative Diabetic Retinopathy ◽  
Screening Program ◽  
Assessment System ◽  
Diabetic Patients ◽  
Fundus Images ◽  
Cross Sectional ◽  
Community Healthcare

Abstract Background: With the diabetes mellitus (DM) prevalence increasing annually, the human grading of retinal images to evaluate DR has posed a substantial burden worldwide. SmartEye is a recently developed fundus image processing and analysis system with lesion quantification function for DR screening. It is sensitive to the lesion area and can automatically identify the lesion position and size. We reported the diabetic retinopathy (DR) grading results of SmartEye versus ophthalmologists in analyzing images captured with non-mydriatic fundus cameras in community healthcare centers, as well as DR lesion quantitative analysis results on different disease stages. Methods: This is a cross-sectional study. All the fundus images were collected from the Shanghai Diabetic Eye Study in Diabetics (SDES) program from Apr 2016 to Aug 2017. 19904 fundus images were acquired from 6013 diabetic patients. The grading results of ophthalmologists and SmartEye are compared. Lesion quantification of several images at different DR stages is also presented. Results: The sensitivity for diagnosing no DR, mild NPDR (non-proliferative diabetic retinopathy), moderate NPDR, severe NPDR, PDR (proliferative diabetic retinopathy) are 86.19%, 83.18%, 88.64%, 89.59%, and 85.02%. The specificity are 63.07%, 70.96%, 64.16%, 70.38%, and 74.79%, respectively. The AUC are PDR, 0.80 (0.79, 0.81); severe NPDR, 0.80 (0.79, 0.80); moderate NPDR, 0.77 (0.76, 0.77); and mild NPDR, 0.78 (0.77, 0.79). Lesion quantification results showed that the total hemorrhage area, maximum hemorrhage area, total exudation area, and maximum exudation area increase with DR severity. Conclusions: SmartEye has a high diagnostic accuracy in DR screening program using non-mydriatic fundus cameras. SmartEye quantitative analysis may be an innovative and promising method of DR diagnosis and grading. Keywords: Diabetic retinopathy, Screening, Digital imaging processing, Lesion quantification, Epidemiology.

scholarly journals The Diagnostic Accuracy of an Intelligent and Automated Fundus Disease Image Assessment System with Lesion Quantitative Function (SmartEye) in Diabetic Patients

2019 ◽  
Author(s):  
Yi XU ◽  
Yongyi WANG ◽  
Bin LIU ◽  
Lin TANG ◽  
Liangqing LV ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Quantitative Analysis ◽  
Diagnostic Accuracy ◽  
Proliferative Diabetic Retinopathy ◽  
Screening Program ◽  
Assessment System ◽  
Diabetic Patients ◽  
Fundus Images ◽  
Cross Sectional ◽  
Community Healthcare

Abstract Background With the diabetes mellitus (DM) prevalence increasing annually, the human grading of retinal images to evaluate DR has posed a substantial burden worldwide. SmartEye is a recently developed fundus image processing and analysis system with lesion quantification function for DR screening. It is sensitive to the lesion area and can automatically identify the lesion position and size. We reported the diabetic retinopathy (DR) grading results of SmartEye versus ophthalmologists in analyzing images captured with non-mydriatic fundus cameras in community healthcare centers, as well as DR lesion quantitative analysis results on different disease stages. Methods This is a cross-sectional study. All the fundus images were collected from the Shanghai Diabetic Eye Study in Diabetics (SDES) program from Apr 2016 to Aug 2017. 19904 fundus images were acquired from 6013 diabetic patients. The grading results of ophthalmologists and SmartEye are compared. Lesion quantification of several images at different DR stages is also presented. Results The sensitivity for diagnosing no DR, mild NPDR (non-proliferative diabetic retinopathy), moderate NPDR, severe NPDR, PDR (proliferative diabetic retinopathy) are 86.19%, 83.18%, 88.64%, 89.59%, and 85.02%. The specificity are 63.07%, 70.96%, 64.16%, 70.38%, and 74.79%, respectively. The AUC are PDR, 0.80 (0.79, 0.81); severe NPDR, 0.80 (0.79, 0.80); moderate NPDR, 0.77 (0.76, 0.77); and mild NPDR, 0.78 (0.77, 0.79). Lesion quantification results showed that the total hemorrhage area, maximum hemorrhage area, total exudation area, and maximum exudation area increase with DR severity. Conclusions SmartEye has a high diagnostic accuracy in DR screening program using non-mydriatic fundus cameras. SmartEye quantitative analysis may be an innovative and promising method of DR diagnosis and grading.

scholarly journals The Diagnostic Accuracy of an Intelligent and Automated Fundus Disease Image Assessment System with Lesion Quantitative Function (SmartEye) in Diabetic Patients

2019 ◽  
Author(s):  
Yi XU ◽  
Yongyi WANG ◽  
Bin LIU ◽  
Lin TANG ◽  
Liangqing LV ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Quantitative Analysis ◽  
Diagnostic Accuracy ◽  
Proliferative Diabetic Retinopathy ◽  
Screening Program ◽  
Assessment System ◽  
Diabetic Patients ◽  
Fundus Images ◽  
Cross Sectional ◽  
Community Healthcare

Abstract Background With the diabetes mellitus (DM) prevalence increasing annually, the human grading of retinal images to evaluate DR has posed a substantial burden worldwide. SmartEye is a recently developed fundus image processing and analysis system with lesion quantification function for DR screening. It is sensitive to the lesion area and can automatically identify the lesion position and size. We reported the diabetic retinopathy (DR) grading results of SmartEye versus ophthalmologists in analyzing images captured with non-mydriatic fundus cameras in community healthcare centers, as well as DR lesion quantitative analysis results on different disease stages. Methods This is a cross-sectional study. All the fundus images were collected from the Shanghai Diabetic Eye Study in Diabetics (SDES) program from Apr 2016 to Aug 2017. 19904 fundus images were acquired from 6013 diabetic patients. The grading results of ophthalmologists and SmartEye are compared. Lesion quantification of several images at different DR stages is also presented. Results The sensitivity for diagnosing no DR, mild NPDR (non-proliferative diabetic retinopathy), moderate NPDR, severe NPDR, PDR (proliferative diabetic retinopathy) are 86.19%, 83.18%, 88.64%, 89.59%, and 85.02%. The specificity are 63.07%, 70.96%, 64.16%, 70.38%, and 74.79%, respectively. The AUC are PDR, 0.80 (0.79, 0.81); severe NPDR, 0.80 (0.79, 0.80); moderate NPDR, 0.77 (0.76, 0.77); and mild NPDR, 0.78 (0.77, 0.79). Lesion quantification results showed that the total hemorrhage area, maximum hemorrhage area, total exudation area, and maximum exudation area increase with DR severity. Conclusions SmartEye has a high diagnostic accuracy in DR screening program using non-mydriatic fundus cameras. SmartEye quantitative analysis may be an innovative and promising method of DR diagnosis and grading.

scholarly journals The Diagnostic Accuracy of an Intelligent and Automated Fundus Disease Image Assessment System with Lesion Quantitative Function (SmartEye) in Diabetic Patients

2019 ◽  
Author(s):  
Yi XU ◽  
Yongyi WANG ◽  
Bin LIU ◽  
Lin TANG ◽  
Liangqing LV ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Quantitative Analysis ◽  
Diagnostic Accuracy ◽  
Proliferative Diabetic Retinopathy ◽  
Screening Program ◽  
Assessment System ◽  
Diabetic Patients ◽  
Fundus Images ◽  
Cross Sectional ◽  
Community Healthcare

Abstract Background With the diabetes mellitus (DM) prevalence increasing annually, the human grading of retinal images to evaluate DR has posed a substantial burden worldwide. SmartEye is a recently developed fundus image processing and analysis system with lesion quantification function for DR screening. It is sensitive to the lesion area and can automatically identify the lesion position and size. We reported the diabetic retinopathy (DR) grading results of SmartEye versus ophthalmologists in analyzing images captured with non-mydriatic fundus cameras in community healthcare centers, as well as DR lesion quantitative analysis results on different disease stages. Methods This is a cross-sectional study. All the fundus images were collected from the Shanghai Diabetic Eye Study in Diabetics (SDES) program from Apr 2016 to Aug 2017. 19904 fundus images were acquired from 6013 diabetic patients. The grading results of ophthalmologists and SmartEye are compared. Lesion quantification of several images at different DR stages is also presented. Results The sensitivity for diagnosing no DR, mild NPDR (non-proliferative diabetic retinopathy), moderate NPDR, severe NPDR, PDR (proliferative diabetic retinopathy) are 86.19%, 83.18%, 88.64%, 89.59%, and 85.02%. The specificity are 63.07%, 70.96%, 64.16%, 70.38%, and 74.79%, respectively. The AUC are PDR, 0.80 (0.79, 0.81); severe NPDR, 0.80 (0.79, 0.80); moderate NPDR, 0.77 (0.76, 0.77); and mild NPDR, 0.78 (0.77, 0.79). Lesion quantification results showed that the total hemorrhage area, maximum hemorrhage area, total exudation area, and maximum exudation area increase with DR severity. Conclusions SmartEye has a high diagnostic accuracy in DR screening program using non-mydriatic fundus cameras. SmartEye quantitative analysis may be an innovative and promising method of DR diagnosis and grading.

Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System

2020 ◽  
Vol 16 (4) ◽  
pp. 397-426
Author(s):  
Muhammad Nadeem Ashraf ◽  
Muhammad Hussain ◽  
Zulfiqar Habib
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Early Stage ◽  
Spherical Shape ◽  
Cost Effective ◽  
Lesion Detection ◽  
Diabetic Patients ◽  
Cad System ◽  
Non Invasive ◽  
Optic Discs

Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.

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

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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