Early Blindness Detection Based on Retinal Images Using Ensemble Learning

• URL: http://arxiv.org/abs/2006.07475v1
• Date: Fri, 12 Jun 2020 21:16:21 GMT
• Title: Early Blindness Detection Based on Retinal Images Using Ensemble Learning
• Authors: Niloy Sikder, Md. Sanaullah Chowdhury, Abu Shamim Mohammad Arif, and Abdullah-Al Nahid
• Abstract summary: Diabetic retinopathy is the primary cause of vision loss among grownup people around the world. Recent developments in the field of Digital Image Processing (DIP) and Machine Learning (ML) have paved the way to use machines in this regard. In this study, a novel early blind detection method has been proposed based on the color information extracted from retinal images using an ensemble learning algorithm.
• Score: 2.099922236065961
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diabetic retinopathy (DR) is the primary cause of vision loss among grownup people around the world. In four out of five cases having diabetes for a prolonged period leads to DR. If detected early, more than 90 percent of the new DR occurrences can be prevented from turning into blindness through proper treatment. Despite having multiple treatment procedures available that are well capable to deal with DR, the negligence and failure of early detection cost most of the DR patients their precious eyesight. The recent developments in the field of Digital Image Processing (DIP) and Machine Learning (ML) have paved the way to use machines in this regard. The contemporary technologies allow us to develop devices capable of automatically detecting the condition of a persons eyes based on their retinal images. However, in practice, several factors hinder the quality of the captured images and impede the detection outcome. In this study, a novel early blind detection method has been proposed based on the color information extracted from retinal images using an ensemble learning algorithm. The method has been tested on a set of retinal images collected from people living in the rural areas of South Asia, which resulted in a 91 percent classification accuracy.

Related papers

• OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition [66.91236298878383]
  Depression Recognition (DR) poses a considerable challenge, especially in the context of privacy concerns. We design a new imaging system to erase the identity information of captured facial images while retain disease-relevant features. It is irreversible for identity information recovery while preserving essential disease-related characteristics necessary for accurate DR.
  arXiv  Detail & Related papers  (2024-02-29T01:20:29Z)
• Deep Semi-Supervised and Self-Supervised Learning for Diabetic Retinopathy Detection [0.0]
  Diabetic retinopathy is one of the leading causes of blindness in the working-age population of developed countries. Deep neural networks have been widely used in automated systems for DR classification on eye fundus images. This paper presents a semi-supervised method that leverages unlabeled images and labeled ones to train a model that detects diabetic retinopathy.
  arXiv  Detail & Related papers  (2022-08-04T02:28:13Z)
• A comprehensive survey on computer-aided diagnostic systems in diabetic retinopathy screening [0.0]
  Diabetes Mellitus (DM) can lead to significant microvasculature disruptions that eventually causes diabetic retinopathy (DR) Our review is intended for anyone, from student to established researcher, who wants to understand what can be accomplished with CAD systems.
  arXiv  Detail & Related papers  (2022-08-03T02:11:42Z)
• RADNet: Ensemble Model for Robust Glaucoma Classification in Color Fundus Images [0.0]
  Glaucoma is one of the most severe eye diseases, characterized by rapid progression and leading to irreversible blindness. Regular glaucoma screenings of the population shall improve early-stage detection, however the desirable frequency of etymological checkups is often not feasible. In our work, we propose an advanced image pre-processing technique combined with an ensemble of deep classification networks.
  arXiv  Detail & Related papers  (2022-05-25T16:48:00Z)
• MTCD: Cataract Detection via Near Infrared Eye Images [69.62768493464053]
  cataract is a common eye disease and one of the leading causes of blindness and vision impairment. We present a novel algorithm for cataract detection using near-infrared eye images. Deep learning-based eye segmentation and multitask network classification networks are presented.
  arXiv  Detail & Related papers  (2021-10-06T08:10:28Z)
• 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)
• 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)

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.