A review on supervised learning methodologies for detection of exudates in diabetic retinopathy

Wasekar, Ujwala W.; Bathla, Ruchika

doi:10.11591/ijeecs.v23.i2.pp837-846

Cited by 3 publications

(2 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Region, area recognition of an object, or boundary, recognition of an object's boundary, which is also known as pattern are the working approaches of shape classification [21]. There are two types of shape classification methods: decision theoretic and structural aspects [22]. Decision theoretic type recognizes quantities such as length, area, and texture [23].…”

Section: Classification Algorithmmentioning

confidence: 99%

Feature extraction to predict quality of segregating sweet tamarind using image processing

Tangwannawit

2022

IJEECS

View full text Add to dashboard Cite

In this modern age, several new methods have been developed, especially in image processing for agriculture business, which consists of technologies derived from artificial intelligence (AI) capabilities called machine learning. Classify is a widely used method to analyze patterns, trends, as well as the body of knowledge from the data visualization. Image classification application improves discrimination and prediction efficiency. The objective of this research was to feature extraction of sweet tamarind and compare the algorithm for classification. This research used images from golden sweet tamarind species with the use of MATLAB and Python language. The steps of this research consisted of 1) preprocessing step for finding the distance to appropriate of the image quality, 2) feature extracting for finding the number of black pixels and the number of white pixels, perimeter, diameter, and centroid, and 3) classifying for algorithms' comparison. The results showed that the camera's distance to the image was 60 cm. The coefficient of determination was at 0.9956, and the Standard Error of Estimate was 7,424.736 pixels. The conclusion of classification found that the random forest had the highest accuracy at 92.00%, SD. = 8.06, precision = 90.12, recall = 92.86, and F1-score = 91.36.

show abstract

Section: Classification Algorithmmentioning

confidence: 99%

Feature extraction to predict quality of segregating sweet tamarind using image processing

Tangwannawit

2022

IJEECS

View full text Add to dashboard Cite

show abstract

“…Hence the accuracy of AVR ratio is also limited by that of segmentation and classification techniques. Recently deep learning-based algorithms outperforms in the field of different medical imaging application [22]- [24]. In this paper, we proposed a deep learning based spatial U-Net for segmentation and classification.…”

Section: Introductionmentioning

confidence: 99%

Quantification of retinal artery-vein ratio for vascular disease diagnosis using spatial U-Net

Pampana

Rayudu

2022

IJEECS

View full text Add to dashboard Cite

The retinal vascular morphological caliber changes reveal the signs of systemic health disorder and life threat diseases such as cardio and cerebral diseases. The quantitative vascular parameters like narrowed arteries, widened venules, reduced artery-vein ratio (AVR) have been associated with aforesaid disorders and diseases. Hence the quantitative biomarker AVR is important parameter in diagnosing variety of diseases. The accurate quantification of AVR be possible if and only if accurate classification of arteries and vein is done. In this paper, we proposed a deep learning based robust vessel segmentation and classification algorithm based on spatial U-Net and the accuracy of the algorithm is 97.8%. In the quantification process, this algorithm is applied on region of interest (RoI) of a fundus image and measured the AVR values using central retinal artery equivalent (CRAE) and central retinal vein equivalent (CRVE). The experimentation is carried on the digital retinal images for vessel extraction (DRIVE) dataset. The outcome of this work is the AVR observed to be >0.4 in normal retinal case and AVR value <0.4 for unhealthy case.

show abstract

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

Bhulakshmi,

Rajput

2024

PeerJ Computer Science

View full text Add to dashboard Cite

Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.

show abstract

A review on supervised learning methodologies for detection of exudates in diabetic retinopathy

Cited by 3 publications

References 46 publications

Feature extraction to predict quality of segregating sweet tamarind using image processing

Feature extraction to predict quality of segregating sweet tamarind using image processing

Quantification of retinal artery-vein ratio for vascular disease diagnosis using spatial U-Net

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

Contact Info

Product

Resources

About