novel approach for classification of diabetics from retinal image using deep learning technique

Umamageswari, A.; Deepa, S.; Beevi, L. Sherin

doi:10.53730/ijhs.v6ns1.5196

Cited by 8 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their findings highlight the potential of using DL algorithms and UWF fundus images for early DR detection. On the other hand, Umamageswari et al [18] considered this a valuable contribution because it presents a novel approach to classifying diabetic patients by analyzing retinal images using DL techniques. Using CNN, the authors analyze retinal images to identify features associated with DR.…”

Section: They Achieved This By Utilizing Statistical Features and Fuz...mentioning

confidence: 99%

Classification of Diabetic Retinopathy by Deep Learning

Al-ahmadi,

Al-ghamdi,

Hsairi

2024

Int. J. Onl. Eng.

View full text Add to dashboard Cite

Diabetic retinopathy (DR), which is a leading cause of adult blindness, primarily affects individuals with diabetes. The manual diagnosis of DR, with the assistance of an ophthalmologist, has proven to be a time-consuming and challenging process. Late detection of DR is a significant factor contributing to the progression of the disease. To address this issue, the present study utilizes deep learning (DL) and transfer learning algorithms to analyze different stages of DR and precisely detect the condition. Using a large dataset comprising approximately 60,000 images, this study employs ResNet-101, DenseNet121, InceptionResNetV2, and EfficientNetB0 DL models to automatically assess the progression of DR. Images of patients’ eyes are inputted into the models, and the DL architectures are adapted to extract relevant features from the eye images. The study’s findings demonstrate that DenseNet121 outperforms ResNet-101, InceptionResNetV2, and EfficientNetB0 in accurately classifying the five stages of DR. The accuracy of the models was 97%, 96%, 95%, and 94%, respectively. These results underscore the effectiveness of DL in achieving an accurate and comprehensive classification of retinitis pigmentosa. By enabling accurate and timely diagnosis of DR, the application of DL techniques significantly contributes to the field of ophthalmology, facilitating improved treatment decisions for patients.

show abstract

Section: They Achieved This By Utilizing Statistical Features and Fuz...mentioning

confidence: 99%

Classification of Diabetic Retinopathy by Deep Learning

Al-ahmadi,

Al-ghamdi,

Hsairi

2024

Int. J. Onl. Eng.

View full text Add to dashboard Cite

show abstract

“…An effective defensive mechanism for the WSN is provided by an intrusion detection system (IDS), a proactive network proper security solution. In this paper, we propose a carefully planned edge that needs to perform penetration testing when the WSN confronts a DoS attack [26]. To achieve this, we introduce the kNN in computer vision and the arithmetic optimization technique (AOA) in evolutionary computation.…”

Section: K-nearest Neighbor (K-nn)mentioning

confidence: 99%

Performance Analysis of Machine Learning-based Detection of Sinkhole Network Layer Attack in MANET

N¹,

Archana²

2022

IJACSA

View full text Add to dashboard Cite

This paper proposes an Intrusion Detection System (IDS) against Sinkhole attacks in Mobile Adhoc Networks (MANET) with mobile sinks. A sinkhole attack is where a hacked node advertises a false routing update to draw network traffic. One effect of a sinkhole attack is that it may be used to launch further attacks, such as drops or changed routing information. Sinkhole nodes attempt to forge the source-destination routes to attract the surrounding network traffic. For this purpose, they modify routing control packets to publish fake routing information that makes sinkhole nodes appear as the best path to some destinations. Several machine learning techniques, including Decision Tree (DT), K-Nearest Neighbor (KNN), Convolution neural network (CNN), and Support Vector Machine (SVM), are used to do the categorization. Furthermore, the MANET's node's characteristics, particularly speed, are used for feature extraction. Totally 3997 unique samples, including 256 malicious samples and 3604 normal samples are collected. The categorization results demonstrate the accuracy of DT, KNN, CNN, and SVM at 98.4%, 96.7%, 98.6%, and 97.8%, respectively. The CNN approach is more accurate than other methods, at 98.6%, based on the data. After that, Priority, SVM, KNN, and CNN, in that order, each denotes excellent accuracy.

show abstract

“…Accordingly, 420 wide-field retinal images were included in the training process for classifying the exudative and non-exudative AMD cases, and the accuracy achieved by the proposed CNN model is 88%. Umamageswari et al ( 2022 ) provided an approach to identify exudates and veins with retinal images for the diagnosis of diabetics. Specifically, a CNN was proposed for retinal image recognition.…”

Section: Introductionmentioning

confidence: 99%

Multi-label classification of retinal disease via a novel vision transformer model

Wang,

Lian,

Jiao

2024

Front. Neurosci.

View full text Add to dashboard Cite

IntroductionThe precise identification of retinal disorders is of utmost importance in the prevention of both temporary and permanent visual impairment. Prior research has yielded encouraging results in the classification of retinal images pertaining to a specific retinal condition. In clinical practice, it is not uncommon for a single patient to present with multiple retinal disorders concurrently. Hence, the task of classifying retinal images into multiple labels remains a significant obstacle for existing methodologies, but its successful accomplishment would yield valuable insights into a diverse array of situations simultaneously.MethodsThis study presents a novel vision transformer architecture called retinal ViT, which incorporates the self-attention mechanism into the field of medical image analysis. To note that this study supposed to prove that the transformer-based models can achieve competitive performance comparing with the CNN-based models, hence the convolutional modules have been eliminated from the proposed model. The suggested model concludes with a multi-label classifier that utilizes a feed-forward network architecture. This classifier consists of two layers and employs a sigmoid activation function.Results and discussionThe experimental findings provide evidence of the improved performance exhibited by the suggested model when compared to state-of-the-art approaches such as ResNet, VGG, DenseNet, and MobileNet, on the publicly available dataset ODIR-2019, and the proposed approach has outperformed the state-of-the-art algorithms in terms of Kappa, F1 score, AUC, and AVG.

show abstract

novel approach for classification of diabetics from retinal image using deep learning technique

Cited by 8 publications

References 0 publications

Classification of Diabetic Retinopathy by Deep Learning

Classification of Diabetic Retinopathy by Deep Learning

Performance Analysis of Machine Learning-based Detection of Sinkhole Network Layer Attack in MANET

Multi-label classification of retinal disease via a novel vision transformer model

Contact Info

Product

Resources

About