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Bhulakshmi D, Rajput DS. A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images. PeerJ Comput Sci 2024; 10:e1947. [PMID: 38699206 PMCID: PMC11065411 DOI: 10.7717/peerj-cs.1947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 02/28/2024] [Indexed: 05/05/2024]
Abstract
Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.
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Affiliation(s)
- Dasari Bhulakshmi
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Dharmendra Singh Rajput
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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Chen Q, Peng J, Zhao S, Liu W. Automatic artery/vein classification methods for retinal blood vessel: A review. Comput Med Imaging Graph 2024; 113:102355. [PMID: 38377630 DOI: 10.1016/j.compmedimag.2024.102355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 02/22/2024]
Abstract
Automatic retinal arteriovenous classification can assist ophthalmologists in disease early diagnosis. Deep learning-based methods and topological graph-based methods have become the main solutions for retinal arteriovenous classification in recent years. This paper reviews the automatic retinal arteriovenous classification methods from 2003 to 2022. Firstly, we compare different methods and provide comparison tables of the summary results. Secondly, we complete the classification of the public arteriovenous classification datasets and provide the annotation development tables of different datasets. Finally, we sort out the challenges of evaluation methods and provide a comprehensive evaluation system. Quantitative and qualitative analysis shows the changes in research hotspots over time, Quantitative and qualitative analyses reveal the evolution of research hotspots over time, highlighting the significance of exploring the integration of deep learning with topological information in future research.
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Affiliation(s)
- Qihan Chen
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Jianqing Peng
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; Guangdong Provincial Key Laboratory of Fire Science and Technology, Guangzhou 510006, China.
| | - Shen Zhao
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
| | - Wanquan Liu
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
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Steffi S, Sam Emmanuel WR. Resilient back-propagation machine learning-based classification on fundus images for retinal microaneurysm detection. Int Ophthalmol 2024; 44:91. [PMID: 38367192 DOI: 10.1007/s10792-024-02982-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 10/29/2023] [Indexed: 02/19/2024]
Abstract
BACKGROUND The timely diagnosis of medical conditions, particularly diabetic retinopathy, relies on the identification of retinal microaneurysms. However, the commonly used retinography method poses a challenge due to the diminutive dimensions and limited differentiation of microaneurysms in images. PROBLEM STATEMENT Automated identification of microaneurysms becomes crucial, necessitating the use of comprehensive ad-hoc processing techniques. Although fluorescein angiography enhances detectability, its invasiveness limits its suitability for routine preventative screening. OBJECTIVE This study proposes a novel approach for detecting retinal microaneurysms using a fundus scan, leveraging circular reference-based shape features (CR-SF) and radial gradient-based texture features (RG-TF). METHODOLOGY The proposed technique involves extracting CR-SF and RG-TF for each candidate microaneurysm, employing a robust back-propagation machine learning method for training. During testing, extracted features from test images are compared with training features to categorize microaneurysm presence. RESULTS The experimental assessment utilized four datasets (MESSIDOR, Diaretdb1, e-ophtha-MA, and ROC), employing various measures. The proposed approach demonstrated high accuracy (98.01%), sensitivity (98.74%), specificity (97.12%), and area under the curve (91.72%). CONCLUSION The presented approach showcases a successful method for detecting retinal microaneurysms using a fundus scan, providing promising accuracy and sensitivity. This non-invasive technique holds potential for effective screening in diabetic retinopathy and other related medical conditions.
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Affiliation(s)
- S Steffi
- Department of Computer Science, Nesamony Memorial Christian College Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamil Nadu, 627012, India.
| | - W R Sam Emmanuel
- Department of PG Computer Science, Nesamony Memorial Christian College Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamil Nadu, 627012, India
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Yuan H, Dai M, Shi C, Li M, Li H. A generative adversarial neural network with multi-attention feature extraction for fundus lesion segmentation. Int Ophthalmol 2023; 43:5079-5090. [PMID: 37851139 DOI: 10.1007/s10792-023-02911-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 09/29/2023] [Indexed: 10/19/2023]
Abstract
PURPOSE Fundus lesion segmentation determines the location and size of diabetes retinopathy in fundus image, which assists doctors in developing the best eye treatment plan. However, owing to the scattered distribution and the similarity of lesions, it is extremely difficult to extract representative lesions feature and accurately segment lesions area. METHODS To solve the thorny problem, a generative adversarial network with multi-attention feature extraction is developed to segment diabetic retinopathy region. The main contributions are as follows: (1) An improved residual U-Net network combining with self-attention mechanism is designed as generative network to fully extract local and global feature of lesions while reducing the loss of key feature information. Considering the correlation between the same lesions feature of different samples, external attention mechanism is introduced in the residual U-Net network to focus on the relevant features of the same lesions in different samples throughout the entire dataset. (2) A discriminative network based on the PatchGAN structure is designed to further enhance the segmentation ability of generation network by discriminating between true and false samples. RESULTS The proposed network is evaluated on the public dataset IDRiD, which achieved the Dice correlation coefficients of 75.7%, 76.53%, 50.06%, and 45.89% for EX, SE, MA, and HE, respectively. CONCLUSION The experimental results show the generative adversarial neural network qualified for accurate segmentation of diabetic retinopathy from fundus image well.
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Affiliation(s)
- Haiying Yuan
- Faculty of Information Technology, Beijing University of Technology, No.100 Pingleyuan, Chaoyang District, Beijing, 100124, People's Republic of China.
| | - Mengfan Dai
- Faculty of Information Technology, Beijing University of Technology, No.100 Pingleyuan, Chaoyang District, Beijing, 100124, People's Republic of China
| | - Cheng Shi
- Faculty of Information Technology, Beijing University of Technology, No.100 Pingleyuan, Chaoyang District, Beijing, 100124, People's Republic of China
| | - Minghao Li
- Faculty of Information Technology, Beijing University of Technology, No.100 Pingleyuan, Chaoyang District, Beijing, 100124, People's Republic of China
| | - Haihang Li
- Faculty of Information Technology, Beijing University of Technology, No.100 Pingleyuan, Chaoyang District, Beijing, 100124, People's Republic of China
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Huda NU, Salam AA, Alghamdi NS, Zeb J, Akram MU. Proliferative Diabetic Retinopathy Diagnosis Using Varying-Scales Filter Banks and Double-Layered Thresholding. Diagnostics (Basel) 2023; 13:2231. [PMID: 37443625 DOI: 10.3390/diagnostics13132231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/09/2023] [Accepted: 06/10/2023] [Indexed: 07/15/2023] Open
Abstract
Diabetic retinopathy is one of the abnormalities of the retina in which a diabetic patient suffers from severe vision loss due to an affected retina. Proliferative diabetic retinopathy (PDR) is the final and most critical stage of diabetic retinopathy. Abnormal and fragile blood vessels start to grow on the surface of the retina at this stage. It causes retinal detachment, which may lead to complete blindness in severe cases. In this paper, a novel method is proposed for the detection and grading of neovascularization. The proposed system first performs pre-processing on input retinal images to enhance the vascular pattern, followed by blood vessel segmentation and optic disc localization. Then various features are tested on the candidate regions with different thresholds. In this way, positive and negative advanced diabetic retinopathy cases are separated. Optic disc coordinates are applied for the grading of neovascularization as NVD or NVE. The proposed algorithm improves the quality of automated diagnostic systems by eliminating normal blood vessels and exudates that might cause hindrances in accurate disease detection, thus resulting in more accurate detection of abnormal blood vessels. The evaluation of the proposed system has been carried out using performance parameters such as sensitivity, specificity, accuracy, and positive predictive value (PPV) on a publicly available standard retinal image database and one of the locally available databases. The proposed algorithm gives an accuracy of 98.5% and PPV of 99.8% on MESSIDOR and an accuracy of 96.5% and PPV of 100% on the local database.
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Affiliation(s)
- Noor Ul Huda
- Center for Advanced Studies in Telecommunications (CAST), COMSATS Institute of Information Technology, Islamabad 45550, Pakistan
| | - Anum Abdul Salam
- Computer and Software Engineering Department, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 24090, Pakistan
| | - Norah Saleh Alghamdi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Jahan Zeb
- Computer and Software Engineering Department, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 24090, Pakistan
| | - Muhammad Usman Akram
- Computer and Software Engineering Department, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 24090, Pakistan
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Venkaiahppalaswamy B, Prasad Reddy PVGD, Batha S. Hybrid deep learning approaches for the detection of diabetic retinopathy using optimized wavelet based model. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Kusakunniran W, Karnjanapreechakorn S, Choopong P, Siriapisith T, Tesavibul N, Phasukkijwatana N, Prakhunhungsit S, Boonsopon S. Detecting and staging diabetic retinopathy in retinal images using multi-branch CNN. APPLIED COMPUTING AND INFORMATICS 2022. [DOI: 10.1108/aci-06-2022-0150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
PurposeThis paper aims to propose a solution for detecting and grading diabetic retinopathy (DR) in retinal images using a convolutional neural network (CNN)-based approach. It could classify input retinal images into a normal class or an abnormal class, which would be further split into four stages of abnormalities automatically.Design/methodology/approachThe proposed solution is developed based on a newly proposed CNN architecture, namely, DeepRoot. It consists of one main branch, which is connected by two side branches. The main branch is responsible for the primary feature extractor of both high-level and low-level features of retinal images. Then, the side branches further extract more complex and detailed features from the features outputted from the main branch. They are designed to capture details of small traces of DR in retinal images, using modified zoom-in/zoom-out and attention layers.FindingsThe proposed method is trained, validated and tested on the Kaggle dataset. The regularization of the trained model is evaluated using unseen data samples, which were self-collected from a real scenario from a hospital. It achieves a promising performance with a sensitivity of 98.18% under the two classes scenario.Originality/valueThe new CNN-based architecture (i.e. DeepRoot) is introduced with the concept of a multi-branch network. It could assist in solving a problem of an unbalanced dataset, especially when there are common characteristics across different classes (i.e. four stages of DR). Different classes could be outputted at different depths of the network.
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Khandouzi A, Ariafar A, Mashayekhpour Z, Pazira M, Baleghi Y. Retinal Vessel Segmentation, a Review of Classic and Deep Methods. Ann Biomed Eng 2022; 50:1292-1314. [PMID: 36008569 DOI: 10.1007/s10439-022-03058-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 08/15/2022] [Indexed: 11/01/2022]
Abstract
Retinal illnesses such as diabetic retinopathy (DR) are the main causes of vision loss. In the early recognition of eye diseases, the segmentation of blood vessels in retina images plays an important role. Different symptoms of ocular diseases can be identified by the geometric features of ocular arteries. However, due to the complex construction of the blood vessels and their different thicknesses, segmenting the retina image is a challenging task. There are a number of algorithms that helped the detection of retinal diseases. This paper presents an overview of papers from 2016 to 2022 that discuss machine learning and deep learning methods for automatic vessel segmentation. The methods are divided into two groups: Deep learning-based, and classic methods. Algorithms, classifiers, pre-processing and specific techniques of each group is described, comprehensively. The performances of recent works are compared based on their achieved accuracy in different datasets in inclusive tables. A survey of most popular datasets like DRIVE, STARE, HRF and CHASE_DB1 is also given in this paper. Finally, a list of findings from this review is presented in the conclusion section.
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Affiliation(s)
- Ali Khandouzi
- Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - Ali Ariafar
- Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - Zahra Mashayekhpour
- Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - Milad Pazira
- Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - Yasser Baleghi
- Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.
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Abstract
Eye fundus images are used in clinical diagnosis for the detection and assessment of retinal disorders. When retinal images are degraded by scattering due to opacities of the eye tissues, the precise detection of abnormalities is complicated depending on the grading of the opacity. This paper presents a concept proof study on the use of the contrast limited adaptive histogram equalization (CLAHE) technique for better visualization of eye fundus images for different levels of blurring due to different stages of cataracts. Processing is performed in three different color spaces: RGB, CIELAB and HSV, with the aim of finding which one better enhances the missed diagnostic features due to blur. The experimental results show that some fundus features not observable by naked eye can be detected in some of the space color processed with the proposed method. In this work, we also develop and provide an online image process, which allows clinicians to tune the default parameters of the algorithm for a better visualization of the characteristics of fundus images. It also allows the choice of a region of interest (ROI) within the images that provide better visualization of some features than those enhanced by the processing of the full picture.
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Hervella ÁS, Rouco J, Novo J, Ortega M. Multimodal image encoding pre-training for diabetic retinopathy grading. Comput Biol Med 2022; 143:105302. [PMID: 35219187 DOI: 10.1016/j.compbiomed.2022.105302] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 01/11/2022] [Accepted: 01/26/2022] [Indexed: 11/18/2022]
Abstract
Diabetic retinopathy is an increasingly prevalent eye disorder that can lead to severe vision impairment. The severity grading of the disease using retinal images is key to provide an adequate treatment. However, in order to learn the diverse patterns and complex relations that are required for the grading, deep neural networks require very large annotated datasets that are not always available. This has been typically addressed by reusing networks that were pre-trained for natural image classification, hence relying on additional annotated data from a different domain. In contrast, we propose a novel pre-training approach that takes advantage of unlabeled multimodal visual data commonly available in ophthalmology. The use of multimodal visual data for pre-training purposes has been previously explored by training a network in the prediction of one image modality from another. However, that approach does not ensure a broad understanding of the retinal images, given that the network may exclusively focus on the similarities between modalities while ignoring the differences. Thus, we propose a novel self-supervised pre-training that explicitly teaches the networks to learn the common characteristics between modalities as well as the characteristics that are exclusive to the input modality. This provides a complete comprehension of the input domain and facilitates the training of downstream tasks that require a broad understanding of the retinal images, such as the grading of diabetic retinopathy. To validate and analyze the proposed approach, we performed an exhaustive experimentation on different public datasets. The transfer learning performance for the grading of diabetic retinopathy is evaluated under different settings while also comparing against previous state-of-the-art pre-training approaches. Additionally, a comparison against relevant state-of-the-art works for the detection and grading of diabetic retinopathy is also provided. The results show a satisfactory performance of the proposed approach, which outperforms previous pre-training alternatives in the grading of diabetic retinopathy.
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Affiliation(s)
- Álvaro S Hervella
- Centro de Investigación CITIC, Universidade da Coruña, A Coruña, Spain; VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, Spain.
| | - José Rouco
- Centro de Investigación CITIC, Universidade da Coruña, A Coruña, Spain; VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, Spain.
| | - Jorge Novo
- Centro de Investigación CITIC, Universidade da Coruña, A Coruña, Spain; VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, Spain.
| | - Marcos Ortega
- Centro de Investigación CITIC, Universidade da Coruña, A Coruña, Spain; VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, Spain.
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Li X, Jiang Y, Zhang J, Li M, Luo H, Yin S. Lesion-attention pyramid network for diabetic retinopathy grading. Artif Intell Med 2022; 126:102259. [DOI: 10.1016/j.artmed.2022.102259] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 01/13/2022] [Accepted: 02/16/2022] [Indexed: 02/01/2023]
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Faura G, Boix-Lemonche G, Holmeide AK, Verkauskiene R, Volke V, Sokolovska J, Petrovski G. Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy-Application of Sensor Arrays and Machine Learning. SENSORS 2022; 22:s22030718. [PMID: 35161465 PMCID: PMC8839630 DOI: 10.3390/s22030718] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/20/2021] [Accepted: 12/26/2021] [Indexed: 12/13/2022]
Abstract
In this review, a selection of works on the sensing of biomarkers related to diabetes mellitus (DM) and diabetic retinopathy (DR) are presented, with the scope of helping and encouraging researchers to design sensor-array machine-learning (ML)-supported devices for robust, fast, and cost-effective early detection of these devastating diseases. First, we highlight the social relevance of developing systematic screening programs for such diseases and how sensor-arrays and ML approaches could ease their early diagnosis. Then, we present diverse works related to the colorimetric and electrochemical sensing of biomarkers related to DM and DR with non-invasive sampling (e.g., urine, saliva, breath, tears, and sweat samples), with a special mention to some already-existing sensor arrays and ML approaches. We finally highlight the great potential of the latter approaches for the fast and reliable early diagnosis of DM and DR.
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Affiliation(s)
- Georgina Faura
- Center for Eye Research, Department of Ophthalmology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Kirkeveien 166, 0450 Oslo, Norway; (G.F.); (G.B.-L.)
- Department of Medical Biochemistry, Institute of Clinical Medicine, University of Oslo, 0424 Oslo, Norway
| | - Gerard Boix-Lemonche
- Center for Eye Research, Department of Ophthalmology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Kirkeveien 166, 0450 Oslo, Norway; (G.F.); (G.B.-L.)
| | | | - Rasa Verkauskiene
- Institute of Endocrinology, Medical Academy, Lithuanian University of Health Sciences, LT-50009 Kaunas, Lithuania;
| | - Vallo Volke
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411 Tartu, Estonia;
- Institute of Biomedical and Transplant Medicine, Department of Medical Sciences, Tartu University Hospital, L. Puusepa Street, 51014 Tartu, Estonia
| | | | - Goran Petrovski
- Center for Eye Research, Department of Ophthalmology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Kirkeveien 166, 0450 Oslo, Norway; (G.F.); (G.B.-L.)
- Department of Ophthalmology, Oslo University Hospital, 0450 Oslo, Norway
- Correspondence: ; Tel.: +47-9222-6158
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MicroNet: microaneurysm detection in retinal fundus images using convolutional neural network. Soft comput 2022. [DOI: 10.1007/s00500-022-06752-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Detection of exudates from clinical fundus images using machine learning algorithms in diabetic maculopathy. Int J Diabetes Dev Ctries 2022. [DOI: 10.1007/s13410-021-01039-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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Guo Y, Peng Y. CARNet: Cascade attentive RefineNet for multi-lesion segmentation of diabetic retinopathy images. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-021-00630-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AbstractDiabetic retinopathy is the leading cause of blindness in working population. Lesion segmentation from fundus images helps ophthalmologists accurately diagnose and grade of diabetic retinopathy. However, the task of lesion segmentation is full of challenges due to the complex structure, the various sizes and the interclass similarity with other fundus tissues. To address the issue, this paper proposes a cascade attentive RefineNet (CARNet) for automatic and accurate multi-lesion segmentation of diabetic retinopathy. It can make full use of the fine local details and coarse global information from the fundus image. CARNet is composed of global image encoder, local image encoder and attention refinement decoder. We take the whole image and the patch image as the dual input, and feed them to ResNet50 and ResNet101, respectively, for downsampling to extract lesion features. The high-level refinement decoder uses dual attention mechanism to integrate the same-level features in the two encoders with the output of the low-level attention refinement module for multiscale information fusion, which focus the model on the lesion area to generate accurate predictions. We evaluated the segmentation performance of the proposed CARNet on the IDRiD, E-ophtha and DDR data sets. Extensive comparison experiments and ablation studies on various data sets demonstrate the proposed framework outperforms the state-of-the-art approaches and has better accuracy and robustness. It not only overcomes the interference of similar tissues and noises to achieve accurate multi-lesion segmentation, but also preserves the contour details and shape features of small lesions without overloading GPU memory usage.
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Simple methods for the lesion detection and severity grading of diabetic retinopathy by image processing and transfer learning. Comput Biol Med 2021; 137:104795. [PMID: 34488028 DOI: 10.1016/j.compbiomed.2021.104795] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/21/2021] [Accepted: 08/21/2021] [Indexed: 02/01/2023]
Abstract
Diabetic retinopathy (DR) has become one of the major causes of blindness. Due to the increased prevalence of diabetes worldwide, diabetic patients exhibit high probabilities of developing DR. There is a need to develop a labor-less computer-aided diagnosis system to support the clinical diagnosis. Here, we attempted to develop simple methods for severity grading and lesion detection from retinal fundus images. We developed a severity grading system for DR by transfer learning with a recent convolutional neural network called EfficientNet-B3 and the publicly available Kaggle Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 training dataset, which includes artificial noise. After removing the blurred and duplicated images from the dataset using a numerical threshold, the trained model achieved specificity and sensitivity values ≳ 0.98 in the identification of DR retinas. For severity grading, the classification accuracy values of 0.84, 0.95, and 0.98 were recorded for the 1st, 2nd, and 3rd predicted labels, respectively. The utility of EfficientNets-B3 for the severity grading of DR as well as the detailed retinal areas referred were confirmed via visual explanation methods of convolutional neural networks. Lesion extraction was performed by applying an empirically defined threshold value to the enhanced retinal images. Although the extraction of blood vessels and detection of red lesions occurred simultaneously, the red and white lesions, including both soft and hard exudates, were clearly extracted. The detected lesion areas were further confirmed with ground truth using the DIARETDB1 database images with general accuracy. The simple and easily applicable methods proposed in this study will aid in the detection and severity grading of DR, which might help in the selection of appropriate treatment strategies for DR.
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Martinez-Murcia FJ, Ortiz A, Ramírez J, Górriz JM, Cruz R. Deep residual transfer learning for automatic diagnosis and grading of diabetic retinopathy. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.04.148] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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Hemorrhage Detection Based on 3D CNN Deep Learning Framework and Feature Fusion for Evaluating Retinal Abnormality in Diabetic Patients. SENSORS 2021; 21:s21113865. [PMID: 34205120 PMCID: PMC8199947 DOI: 10.3390/s21113865] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/29/2021] [Accepted: 06/01/2021] [Indexed: 01/07/2023]
Abstract
Diabetic retinopathy (DR) is the main cause of blindness in diabetic patients. Early and accurate diagnosis can improve the analysis and prognosis of the disease. One of the earliest symptoms of DR are the hemorrhages in the retina. Therefore, we propose a new method for accurate hemorrhage detection from the retinal fundus images. First, the proposed method uses the modified contrast enhancement method to improve the edge details from the input retinal fundus images. In the second stage, a new convolutional neural network (CNN) architecture is proposed to detect hemorrhages. A modified pre-trained CNN model is used to extract features from the detected hemorrhages. In the third stage, all extracted feature vectors are fused using the convolutional sparse image decomposition method, and finally, the best features are selected by using the multi-logistic regression controlled entropy variance approach. The proposed method is evaluated on 1509 images from HRF, DRIVE, STARE, MESSIDOR, DIARETDB0, and DIARETDB1 databases and achieves the average accuracy of 97.71%, which is superior to the previous works. Moreover, the proposed hemorrhage detection system attains better performance, in terms of visual quality and quantitative analysis with high accuracy, in comparison with the state-of-the-art methods.
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A review of diabetic retinopathy: Datasets, approaches, evaluation metrics and future trends. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2021. [DOI: 10.1016/j.jksuci.2021.06.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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21
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Erciyas A, Barışçı N. An Effective Method for Detecting and Classifying Diabetic Retinopathy Lesions Based on Deep Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:9928899. [PMID: 34194538 PMCID: PMC8184323 DOI: 10.1155/2021/9928899] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 05/08/2021] [Indexed: 02/01/2023]
Abstract
Diabetic retinopathy occurs as a result of the harmful effects of diabetes on the eyes. Diabetic retinopathy is also a disease that should be diagnosed early. If not treated early, vision loss may occur. It is estimated that one third of more than half a million diabetic patients will have diabetic retinopathy by the 22nd century. Many effective methods have been proposed for disease detection with deep learning. In this study, unlike other studies, a deep learning-based method has been proposed in which diabetic retinopathy lesions are detected automatically and independently of datasets, and the detected lesions are classified. In the first stage of the proposed method, a data pool is created by collecting diabetic retinopathy data from different datasets. With Faster RCNN, lesions are detected, and the region of interests are marked. The images obtained in the second stage are classified using the transfer learning and attention mechanism. The method tested in Kaggle and MESSIDOR datasets reached 99.1% and 100% ACC and 99.9% and 100% AUC, respectively. When the obtained results are compared with other results in the literature, it is seen that more successful results are obtained.
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Shabbir A, Rasheed A, Shehraz H, Saleem A, Zafar B, Sajid M, Ali N, Dar SH, Shehryar T. Detection of glaucoma using retinal fundus images: A comprehensive review. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:2033-2076. [PMID: 33892536 DOI: 10.3934/mbe.2021106] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Content-based image analysis and computer vision techniques are used in various health-care systems to detect the diseases. The abnormalities in a human eye are detected through fundus images captured through a fundus camera. Among eye diseases, glaucoma is considered as the second leading case that can result in neurodegeneration illness. The inappropriate intraocular pressure within the human eye is reported as the main cause of this disease. There are no symptoms of glaucoma at earlier stages and if the disease remains unrectified then it can lead to complete blindness. The early diagnosis of glaucoma can prevent permanent loss of vision. Manual examination of human eye is a possible solution however it is dependant on human efforts. The automatic detection of glaucoma by using a combination of image processing, artificial intelligence and computer vision can help to prevent and detect this disease. In this review article, we aim to present a comprehensive review about the various types of glaucoma, causes of glaucoma, the details about the possible treatment, details about the publicly available image benchmarks, performance metrics, and various approaches based on digital image processing, computer vision, and deep learning. The review article presents a detailed study of various published research models that aim to detect glaucoma from low-level feature extraction to recent trends based on deep learning. The pros and cons of each approach are discussed in detail and tabular representations are used to summarize the results of each category. We report our findings and provide possible future research directions to detect glaucoma in conclusion.
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Affiliation(s)
- Amsa Shabbir
- Department of Software Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan
| | - Aqsa Rasheed
- Department of Software Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan
| | - Huma Shehraz
- Department of Software Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan
| | - Aliya Saleem
- Department of Software Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan
| | - Bushra Zafar
- Department of Computer Science, Government College University, Faisalabad 38000, Pakistan
| | - Muhammad Sajid
- Department of Electrical Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan
| | - Nouman Ali
- Department of Software Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan
| | - Saadat Hanif Dar
- Department of Software Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan
| | - Tehmina Shehryar
- Department of Software Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan
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Li T, Bo W, Hu C, Kang H, Liu H, Wang K, Fu H. Applications of deep learning in fundus images: A review. Med Image Anal 2021; 69:101971. [PMID: 33524824 DOI: 10.1016/j.media.2021.101971] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 01/12/2021] [Indexed: 02/06/2023]
Abstract
The use of fundus images for the early screening of eye diseases is of great clinical importance. Due to its powerful performance, deep learning is becoming more and more popular in related applications, such as lesion segmentation, biomarkers segmentation, disease diagnosis and image synthesis. Therefore, it is very necessary to summarize the recent developments in deep learning for fundus images with a review paper. In this review, we introduce 143 application papers with a carefully designed hierarchy. Moreover, 33 publicly available datasets are presented. Summaries and analyses are provided for each task. Finally, limitations common to all tasks are revealed and possible solutions are given. We will also release and regularly update the state-of-the-art results and newly-released datasets at https://github.com/nkicsl/Fundus_Review to adapt to the rapid development of this field.
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Affiliation(s)
- Tao Li
- College of Computer Science, Nankai University, Tianjin 300350, China
| | - Wang Bo
- College of Computer Science, Nankai University, Tianjin 300350, China
| | - Chunyu Hu
- College of Computer Science, Nankai University, Tianjin 300350, China
| | - Hong Kang
- College of Computer Science, Nankai University, Tianjin 300350, China
| | - Hanruo Liu
- Beijing Tongren Hospital, Capital Medical University, Address, Beijing 100730 China
| | - Kai Wang
- College of Computer Science, Nankai University, Tianjin 300350, China.
| | - Huazhu Fu
- Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, UAE
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Melo T, Mendonça AM, Campilho A. Microaneurysm detection in color eye fundus images for diabetic retinopathy screening. Comput Biol Med 2020; 126:103995. [PMID: 33007620 DOI: 10.1016/j.compbiomed.2020.103995] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 09/07/2020] [Accepted: 09/07/2020] [Indexed: 02/01/2023]
Abstract
Diabetic retinopathy (DR) is a diabetes complication, which in extreme situations may lead to blindness. Since the first stages are often asymptomatic, regular eye examinations are required for an early diagnosis. As microaneurysms (MAs) are one of the first signs of DR, several automated methods have been proposed for their detection in order to reduce the ophthalmologists' workload. Although local convergence filters (LCFs) have already been applied for feature extraction, their potential as MA enhancement operators was not explored yet. In this work, we propose a sliding band filter for MA enhancement aiming at obtaining a set of initial MA candidates. Then, a combination of the filter responses with color, contrast and shape information is used by an ensemble of classifiers for final candidate classification. Finally, for each eye fundus image, a score is computed from the confidence values assigned to the MAs detected in the image. The performance of the proposed methodology was evaluated in four datasets. At the lesion level, sensitivities of 64% and 81% were achieved for an average of 8 false positives per image (FPIs) in e-ophtha MA and SCREEN-DR, respectively. In the last dataset, an AUC of 0.83 was also obtained for DR detection.
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Affiliation(s)
- Tânia Melo
- Institute for Systems and Computer Engineering, Technology and Science, Campus da Faculdade de Engenharia da Universidade Do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal; Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias, S/n 4200-465, Porto, Portugal.
| | - Ana Maria Mendonça
- Institute for Systems and Computer Engineering, Technology and Science, Campus da Faculdade de Engenharia da Universidade Do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal; Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias, S/n 4200-465, Porto, Portugal
| | - Aurélio Campilho
- Institute for Systems and Computer Engineering, Technology and Science, Campus da Faculdade de Engenharia da Universidade Do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal; Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias, S/n 4200-465, Porto, Portugal
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Wu Z, Shi G, Chen Y, Shi F, Chen X, Coatrieux G, Yang J, Luo L, Li S. Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network. Artif Intell Med 2020; 108:101936. [DOI: 10.1016/j.artmed.2020.101936] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 06/28/2020] [Accepted: 07/20/2020] [Indexed: 02/07/2023]
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26
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Cano J, O’neill WD, Penn RD, Blair NP, Kashani AH, Ameri H, Kaloostian CL, Shahidi M. Classification of advanced and early stages of diabetic retinopathy from non-diabetic subjects by an ordinary least squares modeling method applied to OCTA images. BIOMEDICAL OPTICS EXPRESS 2020; 11:4666-4678. [PMID: 32923070 PMCID: PMC7449717 DOI: 10.1364/boe.394472] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 07/04/2020] [Accepted: 07/12/2020] [Indexed: 05/02/2023]
Abstract
As the prevalence of diabetic retinopathy (DR) continues to rise, there is a need to develop computer-aided screening methods. The current study reports and validates an ordinary least squares (OLS) method to model optical coherence tomography angiography (OCTA) images and derive OLS parameters for classifying proliferative DR (PDR) and no/mild non-proliferative DR (NPDR) from non-diabetic subjects. OLS parameters were correlated with vessel metrics quantified from OCTA images and were used to determine predicted probabilities of PDR, no/mild NPDR, and non-diabetics. The classification rates of PDR and no/mild NPDR from non-diabetic subjects were 94% and 91%, respectively. The method had excellent predictive ability and was validated. With further development, the method may have potential clinical utility and contribute to image-based computer-aided screening and classification of stages of DR and other ocular and systemic diseases.
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Affiliation(s)
- Jennifer Cano
- Department of Ophthalmology, University of Southern California, Los Angeles, CA 90007, USA
| | - William D. O’neill
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Richard D. Penn
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA
- Department of Neurosurgery, Rush University and Hospital, Chicago, IL 60612, USA
| | - Norman P. Blair
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Amir H. Kashani
- Department of Ophthalmology, University of Southern California, Los Angeles, CA 90007, USA
| | - Hossein Ameri
- Department of Ophthalmology, University of Southern California, Los Angeles, CA 90007, USA
| | - Carolyn L. Kaloostian
- Department of Family Medicine, University of Southern California, Los Angeles, CA 90007, USA
| | - Mahnaz Shahidi
- Department of Ophthalmology, University of Southern California, Los Angeles, CA 90007, USA
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Abstract
Ophthalmology is a core medical field that is of interest to many. Retinal examination is a commonly performed diagnostic procedure that can be used to inspect the interior of the eye and screen for any pathological symptoms. Although various types of eye examinations exist, there are many cases where it is difficult to identify the retinal condition of the patient accurately because the test image resolution is very low because of the utilization of simple methods. In this paper, we propose an image synthetic approach that reconstructs the vessel image based on past retinal image data using the multilayer perceptron concept with artificial neural networks. The approach proposed in this study can convert vessel images to vessel-centered images with clearer identification, even for low-resolution retinal images. To verify the proposed approach, we determined whether high-resolution vessel images could be extracted from low-resolution images through a statistical analysis using high- and low-resolution images extracted from the same patient.
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Long S, Chen J, Hu A, Liu H, Chen Z, Zheng D. Microaneurysms detection in color fundus images using machine learning based on directional local contrast. Biomed Eng Online 2020; 19:21. [PMID: 32295576 PMCID: PMC7161183 DOI: 10.1186/s12938-020-00766-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 04/06/2020] [Indexed: 02/07/2023] Open
Abstract
Background As one of the major
complications of diabetes, diabetic retinopathy (DR) is a leading
cause of visual impairment and blindness due to delayed diagnosis
and intervention. Microaneurysms appear as the earliest symptom of
DR. Accurate and reliable detection of microaneurysms in color
fundus images has great importance for DR screening. Methods A microaneurysms' detection method
using machine learning based on directional local contrast (DLC) is
proposed for the early diagnosis of DR. First, blood vessels were
enhanced and segmented using improved enhancement function based on
analyzing eigenvalues of Hessian matrix. Next, with blood vessels
excluded, microaneurysm candidate regions were obtained using shape
characteristics and connected components analysis. After image
segmented to patches, the features of each microaneurysm candidate
patch were extracted, and each candidate patch was classified into
microaneurysm or non-microaneurysm. The main contributions of our
study are (1) making use of directional local contrast in
microaneurysms' detection for the first time, which does make sense
for better microaneurysms' classification. (2) Applying three
different machine learning techniques for classification and
comparing their performance for microaneurysms' detection. The
proposed algorithm was trained and tested on e-ophtha MA database,
and further tested on another independent DIARETDB1 database.
Results of microaneurysms' detection on the two databases were
evaluated on lesion level and compared with existing algorithms. Results The proposed method has achieved better performance compared with existing algorithms on accuracy and computation time. On e-ophtha MA and DIARETDB1 databases, the area under curve (AUC) of receiver operating characteristic (ROC) curve was 0.87 and 0.86, respectively. The free-response ROC (FROC) score on the two databases was 0.374 and 0.210, respectively. The computation time per image with resolution of 2544×1969, 1400×960 and 1500×1152 is 29 s, 3 s and 2.6 s, respectively. Conclusions The proposed method
using machine learning based on directional local contrast of image
patches can effectively detect microaneurysms in color fundus images
and provide an effective scientific basis for early clinical DR
diagnosis.
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Affiliation(s)
- Shengchun Long
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Jiali Chen
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China.
| | - Ante Hu
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Haipeng Liu
- Research Center of Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Coventry, CV1 5RW, UK
| | - Zhiqing Chen
- Eye Center, The second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Dingchang Zheng
- Research Center of Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Coventry, CV1 5RW, UK
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Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images. SENSORS 2020; 20:s20041005. [PMID: 32069912 PMCID: PMC7071097 DOI: 10.3390/s20041005] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 01/30/2020] [Accepted: 02/10/2020] [Indexed: 02/01/2023]
Abstract
Estimated blind people in the world will exceed 40 million by 2025. To develop novel algorithms based on fundus image descriptors that allow the automatic classification of retinal tissue into healthy and pathological in early stages is necessary. In this paper, we focus on one of the most common pathologies in the current society: diabetic retinopathy. The proposed method avoids the necessity of lesion segmentation or candidate map generation before the classification stage. Local binary patterns and granulometric profiles are locally computed to extract texture and morphological information from retinal images. Different combinations of this information feed classification algorithms to optimally discriminate bright and dark lesions from healthy tissues. Through several experiments, the ability of the proposed system to identify diabetic retinopathy signs is validated using different public databases with a large degree of variability and without image exclusion.
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Sengupta S, Singh A, Leopold HA, Gulati T, Lakshminarayanan V. Ophthalmic diagnosis using deep learning with fundus images - A critical review. Artif Intell Med 2019; 102:101758. [PMID: 31980096 DOI: 10.1016/j.artmed.2019.101758] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 11/04/2019] [Accepted: 11/05/2019] [Indexed: 12/23/2022]
Abstract
An overview of the applications of deep learning for ophthalmic diagnosis using retinal fundus images is presented. We describe various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk, optic cup, blood vessels as well as detection of lesions are reviewed. Recent deep learning models for classification of diseases such as age-related macular degeneration, glaucoma, and diabetic retinopathy are also discussed. Important critical insights and future research directions are given.
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Affiliation(s)
- Sourya Sengupta
- Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Ontario, Canada; Department of Systems Design Engineering, University of Waterloo, Ontario, Canada.
| | - Amitojdeep Singh
- Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Ontario, Canada; Department of Systems Design Engineering, University of Waterloo, Ontario, Canada
| | - Henry A Leopold
- Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Ontario, Canada; Department of Systems Design Engineering, University of Waterloo, Ontario, Canada
| | - Tanmay Gulati
- Department of Computer Science and Engineering, Manipal Institute of Technology, India
| | - Vasudevan Lakshminarayanan
- Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Ontario, Canada; Department of Systems Design Engineering, University of Waterloo, Ontario, Canada
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Kou C, Li W, Liang W, Yu Z, Hao J. Microaneurysms segmentation with a U-Net based on recurrent residual convolutional neural network. J Med Imaging (Bellingham) 2019; 6:025008. [PMID: 31259200 PMCID: PMC6582229 DOI: 10.1117/1.jmi.6.2.025008] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Accepted: 05/31/2019] [Indexed: 12/23/2022] Open
Abstract
Microaneurysms (MAs) play an important role in the diagnosis of clinical diabetic retinopathy at the early stage. Annotation of MAs manually by experts is laborious and so it is essential to develop automatic segmentation methods. Automatic MA segmentation remains a challenging task mainly due to the low local contrast of the image and the small size of MAs. A deep learning-based method called U-Net has become one of the most popular methods for the medical image segmentation task. We propose an architecture for U-Net, named deep recurrent U-Net (DRU-Net), obtained by combining the deep residual model and recurrent convolutional operations into U-Net. In the MA segmentation task, DRU-Net can accumulate effective features much better than the typical U-Net. The proposed method is evaluated on two publicly available datasets: E-Ophtha and IDRiD. Our results show that the proposed DRU-Net achieves the best performance with 0.9999 accuracy value and 0.9943 area under curve (AUC) value on the E-Ophtha dataset. And on the IDRiD dataset, it has achieved 0.987 AUC value (to our knowledge, this is the first result of segmenting MAs on this dataset). Compared with other methods, such as U-Net, FCNN, and ResU-Net, our architecture (DRU-Net) achieves state-of-the-art performance.
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Affiliation(s)
- Caixia Kou
- Beijing University of Posts and Telecommunications, Haidian District, Beijing, China
| | - Wei Li
- Beijing University of Posts and Telecommunications, Haidian District, Beijing, China
| | - Wei Liang
- Beijing University of Posts and Telecommunications, Haidian District, Beijing, China
| | - Zekuan Yu
- Peking University, Haidian District, Beijing, China
| | - Jianchen Hao
- Peking University First Hospital, Xicheng District, Beijing, China
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