scholarly journals Deep Learning based Method for Multi-class Classification of Diabetic Retinopathy

2021 ◽  
Vol 2 (2) ◽  
pp. 1-5
Author(s):  
Komal Damodara ◽  
Keyword(s):  
Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Blood Sugar Level ◽  
Retinal Disease ◽  
Fundus Imaging ◽  
Retinal Blood Vessels ◽  
The World ◽  
Multi Class Classification

Diabetes mellitus is a form of diabetes with secondary microvascular complication leading to renal dysfunction and retinal loss also termed as diabetic retinopathy. Retinopathy is grave form of retinal disease. It is the leading cause of blindness in the world. Blockage of tiny minute retinal blood vessels due to the high blood sugar level is the reason why retinopathy leads to blindness or loss of vision. This study serves the purpose of deep learning-based diagnosis of Diabetic retinopathy using the fundus imaging of the eye. In this study architectures such as VGG 16 and VGG 19 are deployed in order to classify the images into 5 categories. The performance of the two models were compared. The highest accuracy is 77.67% when using the VGG 16 pre-trained model.

scholarly journals Existential Methods on Diabetes Detection using Machine Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 3034-3039
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Logistic Regression ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Literature Review ◽  
Learning Techniques ◽  
The World ◽  
Diabetes Prediction

Nowadays, a lot of research is going on in healthcare. One of the significant diseases increased all over the world is Diabetes Mellitus (DM). In this paper, the literature review is done on diabetes prediction using Machine Learning and Deep Learning techniques. Various ML algorithms are used using PIDD (Pima Indian diabetes dataset), and improved k- means using logistic regression among all algorithms achieved the highest accuracy. DL algorithms like CNN and LMST used in diabetic retinopathy images.

Deep Learning-Based Diabetic Retinopathy Detection

2021 ◽  
Vol 11 (3) ◽  
pp. 38-48
Author(s):  
Mohamed Jebran P. ◽  
Sufia Banu
Keyword(s):  
Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Classification Performance ◽  
Learning Problems ◽  
Deep Learning Algorithm ◽  
Lesion Level ◽  
Fundus Photographs

Artificial intelligence (AI) is rapidly evolving from machine learning (ML) to deep learning (DL), which has ignited particular interest in ophthalmology as well. Deep learning has been applied in ophthalmology to fundus photographs, which achieve robust classification performance in the detection of diabetic retinopathy (DR). Diabetic retinopathy is a progressive condition observed in people who have had multiple years of diabetes mellitus. This paper focuses on examining how a deep learning algorithm can be applied for the detection and classification of diabetic retinopathy, both at the image level and at the lesion level. The performance of various neural networks is summarized by taking into account the sensitivity, precision, accuracy with respect to the size of the test datasets. Deep learning problems are discussed at the end.

scholarly journals Deep Learning for Early Diabetic Retinopathy Detection

2020 ◽  
Author(s):  
Anurag Vaidya ◽  
Joshua Stough
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Early Detection ◽  
Early Diagnosis ◽  
Blood Vessels ◽  
Diabetes Complication ◽  
Retinal Blood Vessels ◽  
Multi Class Classification ◽  
Single Neural Network

Diabetic retinopathy (DR)— a leading cause of blindness— is a diabetes complication whichcauses damage to retinal blood vessels. DR can be treated non-invasively if diagnosed early enough.However, early diagnosis requires a medical examination, which may not be possible in some demographicalregions. Much previous work has largely focused on determining the presence of DR and not the severity.Thus, the goal of this study was to develop a single neural network that could 1) detect presence or absence of DR2) perform early detection of DR 3) perform multi-class classification on DR severity.

scholarly journals EfficientNetB3 Architecture for Diabetic Retinopathy Assessment using  Fundus Images

2021 ◽  
Author(s):  
Abdelali ELMOUFIDI ◽  
Hind Amoun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Fundus Images ◽  
Retinal Area ◽  
Hard Exudate ◽  
Retinal Blood Vessels

Abstract Classification of the stages of diabetic retinopathy (DR) is considered a key step in the assessment and management of diabetic retinopathy. Due to the damage caused by high blood sugar to the retinal blood vessels, different microscopic structures can be occupied in the retinal area, such as micro-aneurysms, hard exudate and neovascularization. The convolutional neural network (CNN) based on deep learning has become a promising method for the analysis of biomedical images. In this work, representative images of diabetic retinopathy (DR) are divided into five categories according to the professional knowledge of ophthalmologists. This article focuses on the use of convolutional neural networks to classify background images of DR according to disease severity and on the application of pooling, Softmax Activation to achieve greater accuracy. The aptos2019-blindness-detection database makes it possible to verify the performance of the proposed algorithm.

An effective technique for diabetic retinopathy using hybrid machine learning technique

2021 ◽  
pp. 096228022098354
Author(s):  
N Satyanarayana Murthy ◽  
B Arunadevi
Keyword(s):  
Diabetic Retinopathy ◽  
Short Term Memory ◽  
Early Recognition ◽  
Hybrid Techniques ◽  
Specificity And Sensitivity ◽  
Retinal Blood Vessels ◽  
Segmented Images ◽  
Fuzzy C Means Clustering ◽  
Working Region

Diabetic retinopathy (DR) stays as an eye issue that has continuously developed in individuals who experienced diabetes. The complexities in diabetes cause harm to the vein at the back of the retina. In outrageous cases, DR could swift apparition disaster or visual impairment. This genuine impact had the option to charge through convenient treatment and early recognition. As of late, this issue has been spreading quickly, particularly in the working region, which in the end constrained the interest of an analysis of this disease from the most prompt stage. Therefore, that are castoff to protect the progressions of this disorder, revealing of the retinal blood vessels (RBVs) play a foremost role. The growth of an abnormal vessel leads to the development steps of DR, where it can be well known by extracting the RBV. The recognition of the BV for DR by developing an automatic approach is a major aim of our research study. In the proposed method, there are two major steps: one is segmentation and the second one is classification of affected retinal BV. The proposed method uses the Kinetic Gas Molecule Optimization based on centroid initialization used for the Fuzzy C-means Clustering. In the classification step, those segmented images are given as input to hybrid techniques such as a convolution neural network with bidirectional-long short-term memory (CNN with Bi-LSTM). The learning degree of Bi-LSTM is revised by using the self-attention mechanism for refining the classification accuracy. The trial consequences disclosed that the mixture algorithm achieved higher accuracy, specificity, and sensitivity than existing techniques.

INTEGRATING FUZZY C-MEANS AND MAHALANOBIS METRIC CLASSIFICATION FOR EXUDATE DETECTION IN COLOR FUNDUS IMAGING

2015 ◽  
Vol 15 (05) ◽  
pp. 1550085 ◽  
Author(s):  
MADHURI TASGAONKAR ◽  
MADHURI KHAMBETE
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Ground Truth ◽  
Vital Role ◽  
Detection Accuracy ◽  
Fuzzy C Means ◽  
Fundus Imaging ◽  
Imaging Detection ◽  
Retinal Structure

Diabetes affects retinal structure of a diabetic patient by generating various lesions. Early detection of these lesions can avoid the loss of vision. Automation of detection process can be made easily feasible to masses by the use of fundus imaging. Detection of exudates is significant in diabetic retinopathy (DR) as they are earlier signs and can cause blindness. Finding the exact location as well as correct number of exudates play vital role in the overall treatment of a patient. This paper presents an algorithm for automatic detection of exudates for DR. The algorithm combines the advantages of supervised and unsupervised techniques. It uses fuzzy-C means (FCM) segmentation on coarse level and mahalanobis metric for finer classification of segmented pixels. Mahalanobis criterion gives significance to most relevant features and thus proves a better classifier. The results are validated using DIARETDB0 and DIARETDB1 databases and the ground truth provided with it. This evaluation provided 95.77% detection accuracy.

scholarly journals PATHYAPATHYA IN PRAMEHA

2021 ◽  
Vol 9 (8) ◽  
pp. 1806-1814
Author(s):  
Ripsa Raj K. P ◽  
P Sudhakar Reddy
Keyword(s):  
Diabetes Mellitus ◽  
Scientific Explanation ◽  
Medical Science ◽  
Great Influence ◽  
Clinical Entity ◽  
Causative Factors ◽  
The World ◽  
Growing Population

Diabetes mellitus is becoming the fastest considerable disease in the world. India has been estimated with the fast- est-growing population of Diabetics. According to International Diabetic Federation (IDF), the total number of Diabetic subjects will be around 151 million in India by 2045. The clinical entity of Prameha can be correlated with Diabetes Mellitus. It is considered a Lifestyle disorder, the food, drinks and sedentary habits are having a great influence on this disorder. In Ayurveda classics elaborately mentioned the Nidana (causative factors) and Pathya ahara (wholesome food & drinks) and Apathya ahara (unwholesome food & drinks), Pathya Vihara (Conducive lifestyle) and Apathya Vihara (Non-Conducive lifestyle) of Prameha. The classification of Prameha as per Susruta Samhita as Apathya nimittaja Prameha and Beeja doshaja Prameha indicates the importance of Ahara and Vihara in prevention as well as management of this disease. In this article, a sincere attempt has been made to incorporate the principles of Ayurveda and Modern medical science with a scientific explanation regarding Pathya (Diet) and Vihara (lifestyle) in Prameha (Diabetes Mellitus). Keywords: Pathya, Apathya, Prameha, Ahara, Vihara.

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