Extraction of Disease Area from Retinal Optical Coherence Tomography Images Using Three Dimensional Regional Statistics

Nakahara, Ikunari; Kadir, Mohd Fadzil bin Abdul; Tsuruoka, Shuichi; Takase, Haruhiko; Kawanaka, Hiroharu; Okuyama, Fumio; Matsubara, Hisashi

doi:10.1016/j.procs.2013.09.172

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…In [37], fundus image analysis was conducted using SVM neural networks. It presents a comparative analysis of the recognition accuracy of fundus images from both Japanese and American populations, based on their severity index.…”

Section: ) Discussionmentioning

confidence: 99%

A Hybrid Convolutional Neural Network Model for Automatic Diabetic Retinopathy Classification From Fundus Images

Ali,

Dastgir,

Iqbal

et al. 2023

IEEE J. Transl. Eng. Health Med.

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Objective: Diabetic Retinopathy (DR) is a retinal disease that can cause damage to blood vessels in the eye, that is the major cause of impaired vision or blindness, if not treated early. Manual detection of diabetic retinopathy is time-consuming and prone to human error due to the complex structure of the eye. Methods & Results: various automatic techniques have been proposed to detect diabetic retinopathy from fundus images. However, these techniques are limited in their ability to capture the complex features underlying diabetic retinopathy, particularly in the early stages. In this study, we propose a novel approach to detect diabetic retinopathy using a convolutional neural network (CNN) model. The proposed model extracts features using two different deep learning (DL) models, Resnet50 and Inceptionv3, and concatenates them before feeding them into the CNN for classification. The proposed model is evaluated on a publicly available dataset of fundus images. The experimental results demonstrate that the proposed CNN model achieves higher accuracy, sensitivity, specificity, precision, and f1 score compared to state-of-the-art methods, with respective scores of 96.85%, 99.28%, 98.92%, 96.46%, and 98.65%.INDEX TERMS Diabetic retinopathy, fundus images, machine learning, computer vision. Clinical and Translational Impact Statement-Diabetic Retinopathy is becoming a more common cause of visual impairment in working-age individuals. Optimal results for preventing diabetic vision loss require patients to undergo extensive systemic care. Early detection and treatment are the key to preventing diabetic vision loss, which results from long-term diabetes and causes blood vessel fluid leakage of the retina. Common indicators of DR include blood vessels, exudate, hemorrhages, microaneurysms, and texture. To address this issue, this study proposes a novel CNN model for diabetic retinopathy detection. The proposed approach is an end-to-end mechanism that utilizes Inceptionv3 and Resnet50 for feature extraction of diabetic fundus images. The features extracted from both models are concatenated and input into the proposed InceptionV3 Resnet50 convolutional neural network (IR-CNN) model for retinopathy classification. To improve the performance of the proposed model, several experiments, including image enhancement and data augmentation methods, are conducted. The use of DL models, such as Resnet50 and Inceptionv3, for feature extraction enables the model to capture the complex features underlying diabetic retinopathy, leading to more accurate and reliable classification results. The proposed approach outperformed the existing models for DR detection.

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Section: ) Discussionmentioning

confidence: 99%

A Hybrid Convolutional Neural Network Model for Automatic Diabetic Retinopathy Classification From Fundus Images

Ali,

Dastgir,

Iqbal

et al. 2023

IEEE J. Transl. Eng. Health Med.

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“…Three-dimensional regional statistics are used to detect disease macular area using OCT images. The proposed method is tested on five patients with retinal malady in OCT images which indicates 80.7% accuracy for the anomalous range [ 20 ].…”

Section: Retinal Imagingmentioning

confidence: 99%

A Review on Recent Developments for Detection of Diabetic Retinopathy

2016

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Diabetic retinopathy is caused by the retinal micro vasculature which may be formed as a result of diabetes mellitus. Blindness may appear as a result of unchecked and severe cases of diabetic retinopathy. Manual inspection of fundus images to check morphological changes in microaneurysms, exudates, blood vessels, hemorrhages, and macula is a very time-consuming and tedious work. It can be made easily with the help of computer-aided system and intervariability for the observer. In this paper, several techniques for detecting microaneurysms, hemorrhages, and exudates are discussed for ultimate detection of nonproliferative diabetic retinopathy. Blood vessels detection techniques are also discussed for the diagnosis of proliferative diabetic retinopathy. Furthermore, the paper elaborates a discussion on the experiments accessed by authors for the detection of diabetic retinopathy. This work will be helpful for the researchers and technical persons who want to utilize the ongoing research in this area.

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Automated Computer Aided Detection of Diabetic Retinopathy Using Machine Learning Hybrid Model

Kubde¹,

Mohod²

2021

2021 Sixth International Conference on Image Information Processing (ICIIP)

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Extraction of Disease Area from Retinal Optical Coherence Tomography Images Using Three Dimensional Regional Statistics

Cited by 3 publications

References 5 publications

A Hybrid Convolutional Neural Network Model for Automatic Diabetic Retinopathy Classification From Fundus Images

A Hybrid Convolutional Neural Network Model for Automatic Diabetic Retinopathy Classification From Fundus Images

A Review on Recent Developments for Detection of Diabetic Retinopathy

Automated Computer Aided Detection of Diabetic Retinopathy Using Machine Learning Hybrid Model

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