Diabetic Retinopathy Diagnosis based on Convolutional Neural Network

• URL: http://arxiv.org/abs/2008.00148v1
• Date: Sat, 1 Aug 2020 01:56:04 GMT
• Title: Diabetic Retinopathy Diagnosis based on Convolutional Neural Network
• Authors: Mohammed hamzah abed, Lamia Abed Noor Muhammed, Sarah Hussein Toman
• Abstract summary: Convolutional Neural Network is one of the promise methods, so it was for Diabetic Retinopathy detection. Three public dataset DiaretDB0, DiaretDB1 and DrimDB were used in practical testing. The best accuracy that was achieved: for DiaretDB0 is 100%, DiaretDB1 is 99.495% and DrimDB is 97.55%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diabetic Retinopathy DR is a popular disease for many people as a result of age or the diabetic, as a result, it can cause blindness. therefore, diagnosis of this disease especially in the early time can prevent its effect for a lot of patients. To achieve this diagnosis, eye retina must be examined continuously. Therefore, computer-aided tools can be used in the field based on computer vision techniques. Different works have been performed using various machine learning techniques. Convolutional Neural Network is one of the promise methods, so it was for Diabetic Retinopathy detection in this paper. Also, the proposed work contains visual enhancement in the pre-processing phase, then the CNN model is trained to be able for recognition and classification phase, to diagnosis the healthy and unhealthy retina image. Three public dataset DiaretDB0, DiaretDB1 and DrimDB were used in practical testing. The implementation of this work based on Matlab- R2019a, deep learning toolbox and deep network designer to design the architecture of the convolutional neural network and train it. The results were evaluated to different metrics; accuracy is one of them. The best accuracy that was achieved: for DiaretDB0 is 100%, DiaretDB1 is 99.495% and DrimDB is 97.55%.

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• DDxT: Deep Generative Transformer Models for Differential Diagnosis [51.25660111437394]
  We show that a generative approach trained with simpler supervised and self-supervised learning signals can achieve superior results on the current benchmark. The proposed Transformer-based generative network, named DDxT, autoregressively produces a set of possible pathologies, i.e., DDx, and predicts the actual pathology using a neural network.
  arXiv  Detail & Related papers  (2023-12-02T22:57:25Z)
• Convolutional Neural Network Model for Diabetic Retinopathy Feature Extraction and Classification [6.236743421605786]
  We create a novel CNN model and identifies the severity of Diabetic Retinopathy through fundus image input. We classified 4 known DR features, including micro-aneurysms, cotton wools, exudates, and hemorrhages, through convolutional layers. Our contribution is an interpretable model with similar accuracy to more complex models.
  arXiv  Detail & Related papers  (2023-10-16T20:09:49Z)
• Improving Classification of Retinal Fundus Image Using Flow Dynamics Optimized Deep Learning Methods [0.0]
  Diabetic Retinopathy (DR) refers to a barrier that takes place in diabetes mellitus damaging the blood vessel network present in the retina. It can take some time to perform a DR diagnosis using color fundus pictures because experienced clinicians are required to identify the tumors in the imagery used to identify the illness.
  arXiv  Detail & Related papers  (2023-04-29T16:11:34Z)
• Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders [63.391402501241195]
  glaucoma is challenging to detect since it remains asymptomatic until the symptoms are severe. Early identification of glaucoma is generally made based on functional, structural, and clinical assessments. Deep learning methods have partially solved this dilemma by bypassing the marker identification stage and analyzing high-level information directly to classify the data.
  arXiv  Detail & Related papers  (2021-10-04T16:06:49Z)
• Blindness (Diabetic Retinopathy) Severity Scale Detection [0.0]
  Diabetic retinopathy (DR) is a severe complication of diabetes that can cause permanent blindness. Timely diagnosis and treatment of DR are critical to avoid total loss of vision. We propose a novel deep learning based method for automatic screening of retinal fundus images.
  arXiv  Detail & Related papers  (2021-10-04T11:31:15Z)
• An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
  We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-03-02T13:14:15Z)
• A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability [76.64661091980531]
  People with diabetes are at risk of developing diabetic retinopathy (DR) Computer-aided DR diagnosis is a promising tool for early detection of DR and severity grading. This dataset has 1,842 images with pixel-level DR-related lesion annotations, and 1,000 images with image-level labels graded by six board-certified ophthalmologists.
  arXiv  Detail & Related papers  (2020-08-22T07:48:04Z)
• Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification [0.0]
  Diabetic Retinopathy (DR) is one of the microvascular complications of Diabetes Mellitus, which remains one of the leading causes of blindness worldwide. Computational models based on Conal Neural Networks represent the state of the art for the automatic detection of DR using eye fundus images. In this paper, a hybrid Deep Learning-Gaussian process method for DR diagnosis and uncertainty quantification is presented.
  arXiv  Detail & Related papers  (2020-07-29T04:10:42Z)
• Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks [68.96150598294072]
  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights. It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness. In recent years, there has been a significant effort to automate the diagnosis using deep learning. This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
  arXiv  Detail & Related papers  (2020-04-03T14:07:41Z)
• Diabetic Retinopathy detection by retinal image recognizing [0.0]
  The application development took place through convolutional neural networks, which do digital image processing analyzing each image pixel. The use of VGG-16 as a pre-trained model to the application basis was very useful and the final model accuracy was 82%.
  arXiv  Detail & Related papers  (2020-01-14T16:36:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.