Detection of Proliferative Diabetic Retinopathy in Fundus Images Using Convolution Neural Network

Hassan, H.; Yaakob, Marzuqi; Ismail, Sasni; Rahman, Juwairiyyah Abd; Rusni, Izyani Mat; Zabidi, Azlee; Yassin, Ihsan Mohd; Tahir, Nooritawati Md; Shafie, Suraiya M.

doi:10.1088/1757-899x/769/1/012029

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…In 2020, Hassanet al [19] have recommended Fundus Pictures Using Convolution Neural Networks for Proliferative Diabetic Retinopathy Identification. The three goals of this study were to perform two pre-processing methods on the data set, which involved resizing and normalizing the fundus images; establish a deep learning operations and maintenance Artificial Intelligence (AI) network of feature extraction algorithm for identification of PDR on the fundus images; and evaluate the output classification of the network, which took accuracy, sensitivity, and specificity into account.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Empowered Diabetic Retinopathy Detection and Classification using Retinal Fundus Images

Kuna

Prasad²

2023

IJRITCC

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Diabetic Retinopathy (DR) is a commonly occurring disease among diabetic patients that affects retina lesions and vision. Since DR is irreversible, an earlier diagnosis of DR can considerably decrease the risk of vision loss. Manual detection and classification of DR from retinal fundus images is time-consuming, expensive, and prone to errors, contrasting to CAD models. In recent times, DL models have become a familiar topic in several applications, particularly medical image classification. With this motivation, this paper presents new deep learning-empowered diabetic retinopathy detection and classification (DL-DRDC) model. The DL-DRDC technique aims to recognize and categorize different grades of DR using retinal fundus images. The proposed model involves the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique as a pre-processing stage, which is used to enhance the contrast of the fundus images and improve the low contrast of medical images. Besides, the CLAHE is applied to the L channel of the retina images that have higher contrast. In addition, a deep learning-based Efficient Net-based feature extractor is used to generate feature vectors from pre-processed images. Moreover, a deep neural network (DNN) is used as a classifier model to allocate proper DR stages. An extensive set of experimental analyses takes place using a benchmark MESSIDOR dataset and the results are examined interms of different evaluation parameters. The simulation values highlighted the better DR diagnostic efficiency of the DL-DRDC technique over the recent techniques.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Empowered Diabetic Retinopathy Detection and Classification using Retinal Fundus Images

Kuna

Prasad²

2023

IJRITCC

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“…Their experiment demonstrated that both of these networks are capable of detecting NVD with high accuracy and sensitivity. Abu Hassan et al [27] published a paper in which they developed a CNN for detecting PDR in fundus images. Their CNN achieves an accuracy of 73.81%, a sensitivity of 76%, and a specificity of 69%, respectively.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach for the Detection of Neovascularization in Fundus Images Using Transfer Learning

et al. 2022

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Patients with diabetes are at risk of developing a retinal disorder called Proliferative Diabetic Retinopathy (PDR). One of the main characteristics of PDR is the development of neovascularization, a condition in which abnormal blood vessels are formed on the retina. This condition can cause blindness if it is not detected and treated early. Numerous studies have proposed different image processing techniques for detecting neovascularization in fundus images. However, because of its random growth pattern and small size, neovascularization remains challenging to detect. Hence, deep learning techniques are becoming more prevalent in neovascularization identification because of their ability to perform automatic feature extraction on objects with complex features. In this paper, a method of neovascularization detection based on transfer learning is proposed. The performance of the transfer learning method is investigated using four pre-trained Convolutional Neural Network (CNN), which include AlexNet, GoogLeNet, ResNet18, and ResNet50. In addition, an improved network based on the combination of ResNet18 and GoogLeNet is proposed. Evaluation on 1174 retinal image patches showed that the proposed network could achieve 91.57%, 85.69%, 97.44%, and 97.10% of accuracy, sensitivity, specificity, and precision, respectively. We demonstrated that the proposed method outperforms each individual CNN for neovascularization detection. It also shows better performance compared to another method that utilized deep learning models for feature extraction and Support Vector Machine (SVM) for classification.

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“…However, according to statistics, by the end of 2017, the total number of health personnel in China was 11.749 million, including 3.39 million practicing (assistant) doctors and 901,000 rural doctors. Although there are a large number of practitioners, compared with the goals in the "Healthy China 2030" plan proposed by China, the number of doctors is far from enough, and some doctors in areas with limited medical resources have a low level of education, although the number of rural doctors is limited [7,8]. Therefore, some patients in areas with limited medical resources cannot obtain efficient treatment plans or long-term follow-up treatment.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Recognition of Clinical Features of Diabetes Based on Convolutional Neural Network

Wang

Ping

Yang

2022

Computational and Mathematical Methods in Medicine

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Diabetes mellitus is a common chronic noncommunicable disease, the main manifestation of which is the long-term high blood sugar level in patients due to metabolic disorders. However, due to excessive reliance on the clinical experience of ophthalmologists, our diagnosis takes a long time, and it is prone to missed diagnosis and misdiagnosis. In recent years, with the development of deep learning, its application in the auxiliary diagnosis of diabetic retinopathy has become possible. How to use the powerful feature extraction ability of deep learning algorithm to realize the mining of massive medical data is of great significance. Therefore, under the action of computer-aided technology, this paper processes and analyzes the retinal images of the fundus through traditional image processing and convolutional neural network-related methods, so as to achieve the role of assisting clinical treatment. Based on the admission records of diabetic patients after data analysis and feature processing, this paper uses an improved convolutional neural network algorithm to establish a model for predicting changes in diabetic conditions. The model can assist doctors to judge the patient’s treatment effect by using it based on the case records of inpatient diagnosis and treatment and to predict the risk of readmission of inpatients after discharge. It also can help to judge the effectiveness of the treatment plan. The results of the study show that the model proposed in this paper has a lower probability of misjudging patients with poor recovery as good recovery, and the prediction is more accurate.

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Detection of Proliferative Diabetic Retinopathy in Fundus Images Using Convolution Neural Network

Cited by 5 publications

References 10 publications

Deep Learning Empowered Diabetic Retinopathy Detection and Classification using Retinal Fundus Images

Deep Learning Empowered Diabetic Retinopathy Detection and Classification using Retinal Fundus Images

A Deep Learning Approach for the Detection of Neovascularization in Fundus Images Using Transfer Learning

Analysis and Recognition of Clinical Features of Diabetes Based on Convolutional Neural Network

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