Performance Analysis of Deep-Neural-Network-Based Automatic Diagnosis of Diabetic Retinopathy

Tariq, Hassan; Rashid, Muhammad; Javed, Asfa; Zafar, Eeman; Alotaibi, Saud S.; Zia, Muhammad Yousuf Irfan

doi:10.3390/s22010205

Cited by 39 publications

(31 citation statements)

References 26 publications

(35 reference statements)

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“…Numerous studies have been conducted on disease prediction, including diagnosis, prediction, categorization, and treatment. Numerous ML (machine learning) algorithms [ 11 , 12 , 13 ] have been utilized, according to a recent study, to identify and forecast diseases [ 14 , 15 , 16 ]. They have led to a notable increase in the efficiency and advancement of both conventional and ML approaches.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study

Laila

Mahboob

Khan

et al. 2022

Sensors

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Diabetes is a long-lasting disease triggered by expanded sugar levels in human blood and can affect various organs if left untreated. It contributes to heart disease, kidney issues, damaged nerves, damaged blood vessels, and blindness. Timely disease prediction can save precious lives and enable healthcare advisors to take care of the conditions. Most diabetic patients know little about the risk factors they face before diagnosis. Nowadays, hospitals deploy basic information systems, which generate vast amounts of data that cannot be converted into proper/useful information and cannot be used to support decision making for clinical purposes. There are different automated techniques available for the earlier prediction of disease. Ensemble learning is a data analysis technique that combines multiple techniques into a single optimal predictive system to evaluate bias and variation, and to improve predictions. Diabetes data, which included 17 variables, were gathered from the UCI repository of various datasets. The predictive models used in this study include AdaBoost, Bagging, and Random Forest, to compare the precision, recall, classification accuracy, and F1-score. Finally, the Random Forest Ensemble Method had the best accuracy (97%), whereas the AdaBoost and Bagging algorithms had lower accuracy, precision, recall, and F1-scores.

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Section: Introductionmentioning

confidence: 99%

An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study

Laila

Mahboob

Khan

et al. 2022

Sensors

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“…Authors of Ref. [25,29,31,33,42] employ AlexNet in their works. AlexNet consists of 5 convolutional layers, three pooling layers, and 3 fully connected layers.…”

Section: Convolutional Neural Network Architecture Modelsmentioning

confidence: 99%

“…Ref. [1,29,31,33,42] utilised GoogLeNet, or known as Inception presented by Szegedy et al [15], who introduced the Inception module that allows multiple types of filter size to be used in a single image block [15]. GoogLeNet contains nine inception modules with 4 convolutional layers, 4 max-pooling layers, 3 average pooling layers, 5 fully connected layers, and 3 softmax layers.…”

Section: Convolutional Neural Network Architecture Modelsmentioning

confidence: 99%

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Deep learning in the grading of diabetic retinopathy: A review

Tajudin

Kipli

Mahmood

et al. 2022

IET Computer Vision

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Diabetic Retinopathy (DR) grading into different stages of severity continues to remain a challenging issue due to the complexities of the disease. Diabetic Retinopathy grading classifies retinal images to five levels of severity ranging from 0 to 5, which represents No DR, Mild non-proliferative diabetic retinopathy (NPDR), Moderate NPDR, Severe NPDR, and proliferative diabetic retinopathy. With the advancement of Deep Learning, studies on the application of the Convolutional Neural Network (CNN) in DR grading have been on the rise. High accuracy and sensitivity are the desired outcome of these studies. This paper reviewed recently published studies that employed CNN for DR grading to 5 levels of severity. Various approaches are applied in classifying retinal images which are, (i) by training CNN models to learn the features for each grade and (ii) by detecting and segmenting lesions using information about their location such as microaneurysms, exudates, and haemorrhages. Public and private datasets have been utilised by researchers in classifying retinal images for DR. The performance of the CNN models was measured by accuracy, specificity, sensitivity, and area under the curve. The CNN models and their performance varies for every study. More research into the CNN model is necessary for future work to improve model performance in DR grading. The Inception model can be used as a starting point for subsequent research. It will also be necessary to investigate the attributes that the model uses for grading.

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“…A composite Deep Neural Network (DNN) structure [17] was integrated with a gated-attention strategy for automated prognosis of DR. Deep transfer learning [18] was investigated based on the AlexNet, GoogleNet, InceptionV4, Inception ResNetV2 and ResNext50 to automatically diagnose DR.…”

Section: Related Workmentioning

confidence: 99%

Multi-Scale Attention-Based Mechanism in Gradient Boosting Convolutional Neural Network for Diabetic Retinopathy Grade Classification

2022

IJIES

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Diabetic Retinopathy (DR) is a common complication of Diabetes Mellitus (DM) that produces retinal abnormalities and can lead to blindness if not diagnosed and treated on time. To address this concern, an adaptive Convolutional Neural Network (CNN) model with Gradient Boosting (GB) called ResNetGB has been used from the literature where a Principal Component Analysis (PCA) based Fully Connected (FC) layer is used to capture the discriminative characteristics from the Retinal Fundus (RF) samples. It is essential to extract more effective features to categorize the DR grades. Hence, in this article, the Multi-Scale Attention (MSA) strategy is incorporated into the ResNetGB model for effective DR grade classification. First, the encoder network is used to embed the RF image in a high-level interpretational space in which the mixture of mid and high-level characteristics is considered to enhance the representation. Then, a Multi-Scale Feature Pyramid (MSFP) is added to define the retinal pattern in various localities and the MSA strategy is applied to the high-level interpretation. Moreover, the entire MSA-ResNetGB framework is trained by the cross-entropy loss to categorize the patients with respective DR grades. Finally, the experimental analysis exhibit that the MSA-ResNetGB model achieves the 94.40% and 94.17% accuracy on two benchmark datasets: Kaggle-APTOS and IDRiD, respectively compared to the cutting-edge models.

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Performance Analysis of Deep-Neural-Network-Based Automatic Diagnosis of Diabetic Retinopathy

Cited by 39 publications

References 26 publications

An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study

An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study

Deep learning in the grading of diabetic retinopathy: A review

Multi-Scale Attention-Based Mechanism in Gradient Boosting Convolutional Neural Network for Diabetic Retinopathy Grade Classification

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