Deep Learning and Computer Vision for Glaucoma Detection: A Review
- URL: http://arxiv.org/abs/2307.16528v1
- Date: Mon, 31 Jul 2023 09:49:51 GMT
- Title: Deep Learning and Computer Vision for Glaucoma Detection: A Review
- Authors: Mona Ashtari-Majlan, Mohammad Mahdi Dehshibi, David Masip
- Abstract summary: Glaucoma is the leading cause of irreversible blindness worldwide.
Recent advances in computer vision and deep learning have demonstrated the potential for automated assessment.
We survey recent studies on AI-based glaucoma diagnosis using fundus, optical coherence tomography, and visual field images.
- Score: 0.8379286663107844
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Glaucoma is the leading cause of irreversible blindness worldwide and poses
significant diagnostic challenges due to its reliance on subjective evaluation.
However, recent advances in computer vision and deep learning have demonstrated
the potential for automated assessment. In this paper, we survey recent studies
on AI-based glaucoma diagnosis using fundus, optical coherence tomography, and
visual field images, with a particular emphasis on deep learning-based methods.
We provide an updated taxonomy that organizes methods into architectural
paradigms and includes links to available source code to enhance the
reproducibility of the methods. Through rigorous benchmarking on widely-used
public datasets, we reveal performance gaps in generalizability, uncertainty
estimation, and multimodal integration. Additionally, our survey curates key
datasets while highlighting limitations such as scale, labeling
inconsistencies, and bias. We outline open research challenges and detail
promising directions for future studies. This survey is expected to be useful
for both AI researchers seeking to translate advances into practice and
ophthalmologists aiming to improve clinical workflows and diagnosis using the
latest AI outcomes.
Related papers
- A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [52.228708947607636]
This paper introduces a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods.
The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics.
We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
arXiv Detail & Related papers (2024-06-05T13:40:07Z) - Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary
Task Integration [54.76511683427566]
This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information.
A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction.
The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
arXiv Detail & Related papers (2024-02-16T05:16:20Z) - Detection and Classification of Diabetic Retinopathy using Deep Learning
Algorithms for Segmentation to Facilitate Referral Recommendation for Test
and Treatment Prediction [0.0]
This research paper addresses the critical challenge of diabetic retinopathy (DR), a severe complication of diabetes leading to potential blindness.
The proposed methodology leverages transfer learning with convolutional neural networks (CNNs) for automatic DR detection using a single fundus photograph.
High evaluation scores in Jaccard, F1, recall, precision, and accuracy underscore the model's potential for enhancing diagnostic capabilities in retinal pathology assessment.
arXiv Detail & Related papers (2024-01-05T11:19:24Z) - Exploring Deep Learning Techniques for Glaucoma Detection: A
Comprehensive Review [0.0]
Glaucoma is one of the primary causes of vision loss around the world.
Recent developments in deep learning approaches demonstrate potential in automating glaucoma detection.
The use of deep learning algorithms may significantly improve the efficacy, usefulness, and accuracy of glaucoma detection.
arXiv Detail & Related papers (2023-11-02T17:39:40Z) - Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic
Retinopathy Detection [0.0]
Diabetic Retinopathy (DR) is a significant cause of blindness globally, highlighting the urgent need for early detection and effective treatment.
Recent advancements in Machine Learning (ML) techniques have shown promise in DR detection, but the availability of labeled data often limits their performance.
This research proposes a novel Semi-Supervised Graph Learning SSGL algorithm tailored for DR detection.
arXiv Detail & Related papers (2023-09-02T04:42:08Z) - Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges [58.32937972322058]
"Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image (MedAI 2021)" competitions.
We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic.
arXiv Detail & Related papers (2023-07-30T16:08:45Z) - Unsupervised deep learning techniques for powdery mildew recognition
based on multispectral imaging [63.62764375279861]
This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves.
We focus on unsupervised deep learning techniques applied to multispectral imaging data.
We propose the use of autoencoder architectures to investigate two strategies for disease detection.
arXiv Detail & Related papers (2021-12-20T13:29:13Z) - 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) - Recent advances and clinical applications of deep learning in medical
image analysis [7.132678647070632]
We reviewed and summarized more than 200 recently published papers to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks.
Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical images.
arXiv Detail & Related papers (2021-05-27T18:05:12Z) - Explaining Clinical Decision Support Systems in Medical Imaging using
Cycle-Consistent Activation Maximization [112.2628296775395]
Clinical decision support using deep neural networks has become a topic of steadily growing interest.
clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend.
We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
arXiv Detail & Related papers (2020-10-09T14:39:27Z)
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.