scholarly journals Review of Machine Learning Applications Using Retinal Fundus Images

Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 134
Author(s):  
Yeonwoo Jeong ◽  
Yu-Jin Hong ◽  
Jae-Ho Han
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Age Related Macular Degeneration ◽  
Machine Learning Techniques ◽  
Fundus Image ◽  
Learning Methods ◽  
Age Related ◽  
Automated Screening ◽  
Complex Features ◽  
Screening And Diagnosis

Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed.

scholarly journals Utility of Deep Learning Methods for Referability Classification of Age-Related Macular Degeneration

2018 ◽  
Vol 136 (11) ◽  
pp. 1305 ◽  
Author(s):  
Phillippe Burlina ◽  
Neil Joshi ◽  
Katia D. Pacheco ◽  
David E. Freund ◽  
Jun Kong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Learning Methods ◽  
Age Related

scholarly journals A Systematic Review of Deep Learning Methods Applied to Ocular Images

2019 ◽  
Vol 30 (1) ◽  
pp. 9-26 ◽  
Author(s):  
Oscar Julian Perdomo Charry ◽  
Fabio Augusto González Osorio
Keyword(s):  
Deep Learning ◽  
Macular Degeneration ◽  
Clinical Signs ◽  
Age Related Macular Degeneration ◽  
Ocular Diseases ◽  
Learning Methods ◽  
Age Related ◽  
Box Models ◽  
The One

Artificial intelligence is having an important effect on different areas of medicine, and ophthalmology has not been the exception. In particular, deep learning methods have been applied successfully to the detection of clinical signs and the classification of ocular diseases. This represents a great potential to increase the number of people correctly diagnosed. In ophthalmology, deep learning methods have primarily been applied to eye fundus images and optical coherence tomography. On the one hand, these methods have achieved an outstanding performance in the detection of ocular diseases such as: diabetic retinopathy, glaucoma, diabetic macular degeneration and age-related macular degeneration.  On the other hand, several worldwide challenges have shared big eye imaging datasets with segmentation of part of the eyes, clinical signs and the ocular diagnostic performed by experts. In addition, these methods are breaking the stigma of black-box models, with the delivering of interpretable clinically information. This review provides an overview of the state-of-the-art deep learning methods used in ophthalmic images, databases and potential challenges for ocular diagnosis

scholarly journals DETECTION OF WHALES USING DEEP LEARNING METHODS AND NEURAL NETWORKS

2017 ◽  
Vol 10 (13) ◽  
pp. 489 ◽  
Author(s):  
Saheb Ghosh ◽  
Sathis Kumar B ◽  
Kathir Deivanai
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Data Model ◽  
Natural Habitat ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
Learning Techniques ◽  
Learning Technique ◽  
Extraction Model

Deep learning methods are a great machine learning technique which is mostly used in artificial neural networks for pattern recognition. This project is to identify the Whales from under water Bioacoustics network using an efficient algorithm and data model, so that location of the whales can be send to the Ships travelling in the same region in order to avoid collision with the whale or disturbing their natural habitat as much as possible. This paper shows application of unsupervised machine learning techniques with help of deep belief network and manual feature extraction model for better results.

scholarly journals Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration

2016 ◽  
Author(s):  
Cecilia S Lee ◽  
Doug M Baughman ◽  
Aaron Y Lee
Keyword(s):  
Deep Learning ◽  
Macular Degeneration ◽  
Imaging Modality ◽  
Age Related Macular Degeneration ◽  
Automated Image Analysis ◽  
Clinical Endpoints ◽  
Patient Level ◽  
Age Related ◽  
Peak Sensitivity ◽  
Automated Screening

Objective: The advent of Electronic Medical Records (EMR) with large electronic imaging databases along with advances in deep neural networks with machine learning has provided a unique opportunity to achieve milestones in automated image analysis. Optical coherence tomography (OCT) is the most commonly obtained imaging modality in ophthalmology and represents a dense and rich dataset when combined with labels derived from the EMR. We sought to determine if deep learning could be utilized to distinguish normal OCT images from images from patients with Age-related Macular Degeneration (AMD). Design: EMR and OCT database study Subjects: Normal and AMD patients who had a macular OCT. Methods: Automated extraction of an OCT imaging database was performed and linked to clinical endpoints from the EMR. OCT macula scans were obtained by Heidelberg Spectralis, and each OCT scan was linked to EMR clinical endpoints extracted from EPIC. The central 11 images were selected from each OCT scan of two cohorts of patients: normal and AMD. Cross-validation was performed using a random subset of patients. Receiver operator curves (ROC) were constructed at an independent image level, macular OCT level, and patient level. Main outcome measure: Area under the ROC. Results: Of a recent extraction of 2.6 million OCT images linked to clinical datapoints from the EMR, 52,690 normal macular OCT images and 48,312 AMD macular OCT images were selected. A deep neural network was trained to categorize images as either normal or AMD. At the image level, we achieved an area under the ROC of 92.78% with an accuracy of 87.63%. At the macula level, we achieved an area under the ROC of 93.83% with an accuracy of 88.98%. At a patient level, we achieved an area under the ROC of 97.45% with an accuracy of 93.45%. Peak sensitivity and specificity with optimal cutoffs were 92.64% and 93.69% respectively. Conclusions: Deep learning techniques achieve high accuracy and is effective as a new image classification technique. These findings have important implications in utilizing OCT in automated screening and the development of computer aided diagnosis tools in the future.

scholarly journals Automated analysis of retinal imaging using machine learning techniques for computer vision

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 1573 ◽  
Author(s):  
Jeffrey De Fauw ◽  
Pearse Keane ◽  
Nenad Tomasev ◽  
Daniel Visentin ◽  
George van den Driessche ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Macular Degeneration ◽  
Human Error ◽  
Machine Learning Algorithms ◽  
Age Related Macular Degeneration ◽  
Machine Learning Techniques ◽  
Sound Waves ◽  
Age Related ◽  
Fundus Photographs

There are almost two million people in the United Kingdom living with sight loss, including around 360,000 people who are registered as blind or partially sighted. Sight threatening diseases, such as diabetic retinopathy and age related macular degeneration have contributed to the 40% increase in outpatient attendances in the last decade but are amenable to early detection and monitoring. With early and appropriate intervention, blindness may be prevented in many cases. Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular (“wet”) age-related macular degeneration (wet AMD) and diabetic retinopathy. Two methods of imaging are commonly used: digital photographs of the fundus (the ‘back’ of the eye) and Optical Coherence Tomography (OCT, a modality that uses light waves in a similar way to how ultrasound uses sound waves). Changes in population demographics and expectations and the changing pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone to human error. The application of novel analysis methods may provide a solution to these challenges. This research will focus on applying novel machine learning algorithms to automatic analysis of both digital fundus photographs and OCT in Moorfields Eye Hospital NHS Foundation Trust patients. Through analysis of the images used in ophthalmology, along with relevant clinical and demographic information, Google DeepMind Health will investigate the feasibility of automated grading of digital fundus photographs and OCT and provide novel quantitative measures for specific disease features and for monitoring the therapeutic success.

scholarly journals Automated analysis of retinal imaging using machine learning techniques for computer vision

F1000Research ◽  
2017 ◽  
Vol 5 ◽  
pp. 1573 ◽  
Author(s):  
Jeffrey De Fauw ◽  
Pearse Keane ◽  
Nenad Tomasev ◽  
Daniel Visentin ◽  
George van den Driessche ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Macular Degeneration ◽  
Human Error ◽  
Machine Learning Algorithms ◽  
Age Related Macular Degeneration ◽  
Machine Learning Techniques ◽  
Sound Waves ◽  
Age Related ◽  
Fundus Photographs

There are almost two million people in the United Kingdom living with sight loss, including around 360,000 people who are registered as blind or partially sighted. Sight threatening diseases, such as diabetic retinopathy and age related macular degeneration have contributed to the 40% increase in outpatient attendances in the last decade but are amenable to early detection and monitoring. With early and appropriate intervention, blindness may be prevented in many cases. Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular (“wet”) age-related macular degeneration (wet AMD) and diabetic retinopathy. Two methods of imaging are commonly used: digital photographs of the fundus (the ‘back’ of the eye) and Optical Coherence Tomography (OCT, a modality that uses light waves in a similar way to how ultrasound uses sound waves). Changes in population demographics and expectations and the changing pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone to human error. The application of novel analysis methods may provide a solution to these challenges. This research will focus on applying novel machine learning algorithms to automatic analysis of both digital fundus photographs and OCT in Moorfields Eye Hospital NHS Foundation Trust patients. Through analysis of the images used in ophthalmology, along with relevant clinical and demographic information, DeepMind Health will investigate the feasibility of automated grading of digital fundus photographs and OCT and provide novel quantitative measures for specific disease features and for monitoring the therapeutic success.

scholarly journals Survey on Applications of Deep Learning and Machine Learning Techniques for Cyber Security

2020 ◽  
Vol 14 (16) ◽  
pp. 210
Author(s):  
Mohammed I. Alghamdi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Cyber Security ◽  
Secondary Data ◽  
Extensive Study ◽  
Machine Learning Techniques ◽  
Working Mechanism ◽  
Learning Methods ◽  
Learning Techniques ◽  
Future Direction

<p class="0abstract">The research aimed to conduct an extensive study of machine learning and deep learning methods in cybersecurity. To accomplish the objectives, the research carried out a qualitative study based on secondary data collection to review the available studies and literature. The research has examined three machine learning methods and three deep learning methods to study the most popular techniques used in cybersecurity. During the research, the working mechanism of each method was studied along with their strengths and weaknesses. Also, a comparative discussion has been made to examine the most effective method for cybersecurity. Limitations in the current literature were also identified, and future direction is also given to target and develop the weak areas of machine learning and deep learning methods.<em> </em><strong></strong></p>

scholarly journals Multi-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS

2021 ◽  
Author(s):  
Gregory C Ghahramani ◽  
Matthew Brendel ◽  
Mingquan Lin ◽  
Qingyu Chen ◽  
Tiarnan Keenan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Longitudinal Data ◽  
Vision Loss ◽  
Severe Form ◽  
Image Features ◽  
Age Related Macular Degeneration ◽  
Age Related ◽  
Fundus Photographs ◽  
Complex Features ◽  
Patients Experience

Age-related macular degeneration (AMD) is the leading cause of vision loss. Some patients experience vision loss over a delayed timeframe, others at a rapid pace. Physicians analyze time-of-visit fundus photographs to predict patient risk of developing late-AMD, the most severe form of AMD. Our study hypothesizes that 1) incorporating historical data improves predictive strength of developing late-AMD and 2) state-of-the-art deep-learning techniques extract more predictive image features than clinicians do. We incorporate longitudinal data from the Age-Related Eye Disease Studies and deep-learning extracted image features in survival settings to predict development of late-AMD. To extract image features, we used multi-task learning frameworks to train convolutional neural networks. Our findings show 1) incorporating longitudinal data improves prediction of late-AMD for clinical standard features, but only the current visit is informative when using complex features and 2) "deep-features" are more informative than clinician derived features. We make codes publicly available at https://github.com/bionlplab/AMD_prognosis_amia2021.

scholarly journals CAD-Based Machine Learning Project for Reducing Human-Factor-Related Errors in Medical Image Analysis

2020 ◽  
pp. 164-172 ◽  
Author(s):  
Adekanmi Adeyinka Adegun ◽  
Roseline Oluwaseun Ogundokun ◽  
Marion Olubunmi Adebiyi ◽  
Emmanuel Oluwatobi Asani
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Skin Cancer ◽  
Human Factor ◽  
Medical Images ◽  
Region Of Interest ◽  
Machine Learning Techniques ◽  
Age Related ◽  
Learning Techniques

Machine learning techniques such as deep learning methods have produced promising results in medical images analysis. This work proposes a user-friendly system that utilizes deep learning techniques for detecting and diagnosing diseases using medical images. This includes the design of CAD-based project that can reduce human factor-related errors while performing manual screening of medical images. The system accepts medical images as input and performs segmentation of the images. Segmentation process analyzes and identifies the region of interest (ROI) of diseases from medical images. Analyzing and segmentation of medical images has assisted in the diagnosis and monitoring of some diseases. Diseases such as skin cancer, age-related fovea degeneration, diabetic retinopathy, glaucoma, hypertension, arteriosclerosis, and choroidal neovascularization can be effectively managed by the analysis of skin lesion and retinal vessels images. The proposed system was evaluated on diseases such as diabetic retinopathy from retina images and skin cancer from dermoscopic images.

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