Automatic Screening and Classification of Diabetic Retinopathy

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Untreated diabetic retinopathy, a condition brought on by unmanaged chronic diabetes, can result in total blindness. In order to avoid the serious side effects of diabetic retinopathy, early medical diagnosis of diabetic retinopathy and its medical treatment are imperative. Ophthalmologists must spend a lot of time manually diagnosing diabetic retinopathy, and patients must endure a lot of discomfort throughout this process. With the use of an automated technology, we can rapidly identify diabetic retinopathy and conveniently continue treatment to prevent further damage to the eye. Exudates, haemorrhages, and micro aneurysms are three features that this study suggests extracting using machine learning. These features are then classified using a hybrid classifier, which combines support vector machines, k nearest neighbours, random forests.

International Journal of Engineering Technology and Management Sciences, 2024

Diabetic Retinopathy (DR) is one of the major causes of blindness. DR mutilates the retinal blood vessels of a patient having diabetes. The DR has two major types: First one is Non-Proliferative Diabetic Retinopathy (NPDR) and second is Proliferative Diabetic Retinopathy (PDR). PDR is the advanced stage of DR which leads to neo vascularization, it is expected that the number of DR patients is to increase from 382 million to 592 million by 2028. In the early stages of the DR the patients were asymptomatic but in advanced stages, it leads to floaters, blurred vision, distortions, and progressive visual acuity loss. It is difficult but utmost important to detect the DR in early stages to avoid the worse effect of latter stages. The colour fundus images were used for the diagnosis of DR, the manual analysis could only be done by highly trained domain experts but it is bit expensive in terms of time and cost. Hence, it is important to use computer vision methods to automatically analyse the fundus images of Retina and assist the physicians/radiologists. The computer vision-based methods are divided into hand-on engineering and end-to-end learning. The hand-on engineering methods extract features using traditional approaches such as HoG, SIFT, LBP, Gabor filters, which failed to encode the variations in scale, rotation, and illumination. The end-to-end leaning automatically learns the hidden rich features and thus performs better classification.

2021

Diabetic retinopathy is a retinal disease that affects diabetes patients and the major cause of blindness for age population. It is an asymptomatic disease, which involves changes to blood vessels that can cause them to bleed or leak fluid, causing distortion of vision. Therefore, the blood vessels extraction is very important to help ophthalmologists to recognize this disease at the first stage in order to prevent an eventual loss of vision. Diabetic Retinopathy (DR) is a leading disabling chronic disease and one of the main causes of blindness and visual impairment in developed countries for diabetic patients. Studies reported that 90% of the cases can be from through early detection and treatment. Eye screening through retinal images is used by physicians to detect the lesions related with this disease. Due to the increasing number of diabetic people, the amount of images to be manually analyzed is becoming unaffordable. Moreover, training new personnel for this type of image-based diagnosis is long, because it requires to acquire expertise by daily practice. This paper presents the survey of retinopathy classification for diabetic patient using various methods which include image processing and artificial intelligence or machine learning.

International Journal of Engineering and Advanced Technology, 2020

Diabetes is a worldwide spread disease which is increasing rapidly and found in all age people. Diabetic Retinopathy is a retinal abnormality caused by diabetes. Which can lead to permanent vision loss or blindness. As Diabetic Retinopathy pathology damages retina without any early symptoms, it is very important to do the regular screening of retina and detection of Retinopathy. Ophthalmologist does the identification of Retinopathy manually which is time consuming and error prone. Hence, there is a need for early and correct automatic detection of Diabetic Retinopathy. Many researches have done for detection using Image Processing, Artificial Intelligence, Neural Network and Machine Learning. This paper presents a review on Diabetic Retinopathy Detection systems. This review highlights the public datasets available for the evaluation of the detection systems with different segmentation and classification techniques. We have discussed the analysis of different classification and seg...

Journal of Computational Science, 2017

Highlights  Automated system for detection and classification of EXs with fundus image modalities  Selection of features set for each candidate lesion using four main descriptors  Combination of different classifiers on features set to improve system accuracy  Evaluation of proposed method by calculating its AUC and ACC with existing methods  Suggested technique outperformed the existing similar approach methods

Diabetic Retinopathy is human eye disease which causes damage to retina of eye and it may eventually lead to complete blindness. Detection of diabetic retinopathy in early stage is essential to avoid complete blindness. Many physical tests like visual acuity test, pupil dilation, optical coherence tomography can be used to detect diabetic retinopathy but they are very time consuming and costly.There are many features present in retina but the exudates feature which is one of the primary signs of diabetic retinopathy and which is a main cause of blindness that could be prevented with the help of this automatic detection process. In feature extraction Pupil dilation is important step in the normal screening process but this affects Diabetes vision. The automatic detection process reduces examination time, and increase accuracy. In this paper we provide review on many techniques and algorithms that helps to diagnose Diabetic Retinopathy in retinal fundus images. This paper also reviews, classifies and compares the algorithms and techniques previously proposed in order to develop better and more effective algorithms.

Computers in biology and medicine, 2017

This paper presents a methodology and first results of an automatic detection system of first signs of Diabetic Retinopathy (DR) in fundus images, developed for the Health Ministry of the Andalusian Regional Government (Spain). The system detects the presence of microaneurysms and haemorrhages in retinography by means of techniques of digital image processing and supervised classification. Evaluation was conducted on 1058 images of 529 diabetic patients at risk of presenting evidence of DR (an image of each eye is provided). To this end, a ground-truth diagnosis was created based on gradations performed by 3 independent ophthalmology specialists. The comparison between the diagnosis provided by the system and the reference clinical diagnosis shows that the system can work at a level of sensitivity that is similar to that achieved by experts (0.9380 sensitivity per patient against 0.9416 sensitivity of several specialists). False negatives have proven to be mild cases. Moreover, whil...

Diabetic retinopathy is spreading dangerously worldwide among people with diabetes, leading to reduced vision and completely blindness. In this paper, a technique is proposed to identify diabetic retinopathy using the fundus image obtained from the patient's retina. The method involves processing the digital image on the fundus image, which help the ophthalmologist in examine DR. Micro-aneurysm, which is considered as first stage of diabetic retinopathy has been diagnosed using Neural Network. The proposed Support Vector Machine has been compared to existing methods-Naive Bayes classifier. Experimental validation is done with MATLAB/SIMULINK software. The preprocess image has been applied as input data for neural network pattern recognition. A significant improvement has been observed in terms of Sensitivity, Specificity and Accuracy compared to the aforementioned existing methods.

ZANCO JOURNAL OF PURE AND APPLIED SCIENCES

Diabetic retinopathy (DR) is considered as one of the global diseases of blindness, especially for aged people. The main reason behind this disease is the complication of diabetes in retinal blood vessels. Usually, the early warning signs are not observed. Screening is an important key for the diagnosis of early stages of diabetic retinopathy. In this work, a new technique for automatically screening three categories; Normal, Non-Proliferative Diabetic Retinopathy (Non-PDR), and Proliferative Diabetic Retinopathy (PDR) disease is presented that is may help doctors and physicians to make a preliminary decision. Neutrosophic set (NS) domain based on statistical features, Gray Level Cooccurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), and difference statistics were used for features extraction. More than thirty statistical textural features derived from the NS set domain and spatial domain have been tested using a features selection scheme named one-way analysis of variables (ANOVA1) with significance value (p<0.001). After feature selection, about sixteen features were passed the test and introduced to the classification stage which is made up of three techniques Multi-class support vector machine (MUSVM), Naïve Bayes (NB), and Decision Forest (DF) classifiers. Over 50 images from each category were downloaded from Digital Retinal Images for Vessel Extraction (DRIVE) database. The performance resulted for this proposed method shows the system robustness in identifying each stage of diabetic retinopathy within the accuracy, sensitivity, and specificity about 95.5%, 100%, and 93.3% respectively. The results of this method were compared with other considered systems. The fair comparison of results shows system superiority and can be used in clinical observation.

2013

Diabetic Retinopathy (DR) is the leading cause of blindness in adults around the world today. Early detection (that is, screening) and timely treatment have been shown to prevent visual loss and blindness in patients with retinal complications of diabetes. The basis of the classification of different stages of diabetic retinopathy is the detection and quantification of blood vessels and hemorrhages present in the retinal image. In this paper, the four retinal abnormalities (microaneurysms, haemorrhages, exudates, and cotton wool spots) are located in 100 color retinal images, previously graded by an ophthalmologist. A new automatic algorithm has been developed and applied to 100 retinal images. Accuracy assessment of the classified output revealed the detection rate of the microaneurysms was 87% using the thresholding method, whereas the detection rate for the haemorrhages was 88%. On the other hand, the correct classification rate for microaneurysms and haemorrhages using the minimum distance classifier was 60% and 94% respectively. The thresholding method resulted in a correct detection rate for exudates and cotton wool spots of 93% and 89% respectively. The minimum distance classifier gave a correct rate for exudates and cotton wool spots of 95% and 86% respectively.