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Amin J, Shazadi I, Sharif M, Yasmin M, Almujally NA, Nam Y. Localization and grading of NPDR lesions using ResNet-18-YOLOv8 model and informative features selection for DR classification based on transfer learning. Heliyon 2024; 10:e30954. [PMID: 38779022 PMCID: PMC11109848 DOI: 10.1016/j.heliyon.2024.e30954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 05/04/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
Complications in diabetes lead to diabetic retinopathy (DR) hence affecting the vision. Computerized methods performed a significant role in DR detection at the initial phase to cure vision loss. Therefore, a method is proposed in this study that consists of three models for localization, segmentation, and classification. A novel technique is designed with the combination of pre-trained ResNet-18 and YOLOv8 models based on the selection of optimum layers for the localization of DR lesions. The localized images are passed to the designed semantic segmentation model on selected layers and trained on optimized learning hyperparameters. The segmentation model performance is evaluated on the Grand-challenge IDRID segmentation dataset. The achieved results are computed in terms of mean IoU 0.95,0.94, 0.96, 0.94, and 0.95 on OD, SoftExs, HardExs, HAE, and MAs respectively. Another classification model is developed in which deep features are derived from the pre-trained Efficientnet-b0 model and optimized using a Genetic algorithm (GA) based on the selected parameters for grading of NPDR lesions. The proposed model achieved greater than 98 % accuracy which is superior to previous methods.
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Affiliation(s)
- Javaria Amin
- Department of Computer Science, University of Wah, Wah Cantt, Pakistan
| | - Irum Shazadi
- Department of Computer Science, University of Wah, Wah Cantt, Pakistan
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | - Mussarat Yasmin
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | - Nouf Abdullah Almujally
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Yunyoung Nam
- Department of ICT Convergence, Soonchunhyang University, Asan, 31538, South Korea
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2
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Khan AQ, Sun G, Khalid M, Imran A, Bilal A, Azam M, Sarwar R. A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification. PLoS One 2024; 19:e0303094. [PMID: 38768222 PMCID: PMC11147523 DOI: 10.1371/journal.pone.0303094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 04/18/2024] [Indexed: 05/22/2024] Open
Abstract
In response to the growing number of diabetes cases worldwide, Our study addresses the escalating issue of diabetic eye disease (DED), a significant contributor to vision loss globally, through a pioneering approach. We propose a novel integration of a Genetic Grey Wolf Optimization (G-GWO) algorithm with a Fully Convolutional Encoder-Decoder Network (FCEDN), further enhanced by a Kernel Extreme Learning Machine (KELM) for refined image segmentation and disease classification. This innovative combination leverages the genetic algorithm and grey wolf optimization to boost the FCEDN's efficiency, enabling precise detection of DED stages and differentiation among disease types. Tested across diverse datasets, including IDRiD, DR-HAGIS, and ODIR, our model showcased superior performance, achieving classification accuracies between 98.5% to 98.8%, surpassing existing methods. This advancement sets a new standard in DED detection and offers significant potential for automating fundus image analysis, reducing reliance on manual examination, and improving patient care efficiency. Our findings are crucial to enhancing diagnostic accuracy and patient outcomes in DED management.
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Affiliation(s)
- Abdul Qadir Khan
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Guangmin Sun
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Majdi Khalid
- Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Azhar Imran
- Department of Creative Technologies, Air University, Islamabad, Pakistan
| | - Anas Bilal
- College of Information Science and Technology, Hainan Normal University, Haikou, China
- Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, China
| | - Muhammad Azam
- Department of Computer Science, Superior University, Lahore, Pakistan
| | - Raheem Sarwar
- OTEHM, Manchester Metropolitan University, Manchester, United Kingdom
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3
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Bilal A, Imran A, Baig TI, Liu X, Abouel Nasr E, Long H. Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization. Sci Rep 2024; 14:10714. [PMID: 38730250 PMCID: PMC11087531 DOI: 10.1038/s41598-024-61322-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/03/2024] [Indexed: 05/12/2024] Open
Abstract
A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition.
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Affiliation(s)
- Anas Bilal
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
- Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, 571158, China
| | - Azhar Imran
- Department of Creative Technologies, Air University, Islamabad, 44000, Pakistan
| | - Talha Imtiaz Baig
- School of Life Science and Technology, University of Electronic Science and Technology of China UESTC, Chengdu, Sichuan, China
| | - Xiaowen Liu
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
| | - Emad Abouel Nasr
- Industrial Engineering Department, College of Engineering, King Saud University, 11421, Riyadh, Saudi Arabia
| | - Haixia Long
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
- Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, 571158, China.
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4
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Bilal A, Liu X, Shafiq M, Ahmed Z, Long H. NIMEQ-SACNet: A novel self-attention precision medicine model for vision-threatening diabetic retinopathy using image data. Comput Biol Med 2024; 171:108099. [PMID: 38364659 DOI: 10.1016/j.compbiomed.2024.108099] [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: 10/26/2023] [Revised: 02/02/2024] [Accepted: 02/02/2024] [Indexed: 02/18/2024]
Abstract
In the realm of precision medicine, the potential of deep learning is progressively harnessed to facilitate intricate clinical decision-making, especially when navigating multifaceted datasets encompassing Omics, Clinical, image, device, social, and environmental dimensions. This study accentuates the criticality of image data, given its instrumental role in detecting and classifying vision-threatening diabetic retinopathy (VTDR) - a predominant global contributor to vision impairment. The timely identification of VTDR is a linchpin for efficacious interventions and the mitigation of vision loss. Addressing this, This study introduces "NIMEQ-SACNet," a novel hybrid model by the prowess of the Enhanced Quantum-Inspired Binary Grey Wolf Optimizer (EQI-BGWO) with a self-attention capsule network. The proposed approach is characterized by two pivotal advancements: firstly, the augmentation of the Binary Grey Wolf Optimization through Quantum Computing methodologies, and secondly, the deployment of the enhanced EQI-BGWO to adeptly calibrate the SACNet's parameters, culminating in a notable uplift in VTDR classification accuracy. The proposed model's ability to handle binary, 5-stage, and 7-stage VTDR classifications adroitly is noteworthy. Rigorous assessments on the fundus image dataset, underscored by metrics such as Accuracy, Sensitivity, Specificity, Precision, F1-Score, and MCC, bear testament to NIMEQ-SACNet's pre-eminence over prevailing algorithms and classification frameworks.
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Affiliation(s)
- Anas Bilal
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
| | - Xiaowen Liu
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
| | - Muhammad Shafiq
- School of Information Engineering, Qujing Normal University, Sichuan, China
| | - Zohaib Ahmed
- Department of Criminology and Forensic Sciences, Lahore Garrison University, Lahore, Pakistan
| | - Haixia Long
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
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5
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Bilal A, Imran A, Baig TI, Liu X, Long H, Alzahrani A, Shafiq M. Improved Support Vector Machine based on CNN-SVD for vision-threatening diabetic retinopathy detection and classification. PLoS One 2024; 19:e0295951. [PMID: 38165976 PMCID: PMC10760665 DOI: 10.1371/journal.pone.0295951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 12/01/2023] [Indexed: 01/04/2024] Open
Abstract
The integration of artificial intelligence (AI) in diagnosing diabetic retinopathy, a major contributor to global vision impairment, is becoming increasingly pronounced. Notably, the detection of vision-threatening diabetic retinopathy (VTDR) has been significantly fortified through automated techniques. Traditionally, the reliance on manual analysis of retinal images, albeit slow and error-prone, constituted the conventional approach. Addressing this, our study introduces a novel methodology that amplifies the robustness and precision of the detection process. This is complemented by the groundbreaking Hierarchical Block Attention (HBA) and HBA-U-Net architecture, which notably propel attention mechanisms in image segmentation. This innovative model refines image processing without imposing excessive computational demands by honing in on individual pixel intricacies, spatial relationships, and channel-specific attention. Building upon this innovation, our proposed method employs a multi-stage strategy encompassing data pre-processing, feature extraction via a hybrid CNN-SVD model, and classification employing an amalgamation of Improved Support Vector Machine-Radial Basis Function (ISVM-RBF), DT, and KNN techniques. Rigorously tested on the IDRiD dataset classified into five severity tiers, the hybrid model yields remarkable performance, achieving a 99.18% accuracy, 98.15% sensitivity, and 100% specificity in VTDR detection, thus surpassing existing methods. These results underscore a more potent avenue for diagnosing and addressing this crucial ocular condition while underscoring AI's transformative potential in medical care, particularly in ophthalmology.
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Affiliation(s)
- Anas Bilal
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
| | - Azhar Imran
- Department of Creative Technologies, Air University, Islamabad, Pakistan
| | - Talha Imtiaz Baig
- School of Life Science and Technology, University of Electronic Science and Technology of China UESTC, Chengdu, Sichuan, China
- School of Science and Technology, University of Management and Technology UMT, Lahore, Punjab, Pakistan
| | - Xiaowen Liu
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
| | - Haixia Long
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
| | - Abdulkareem Alzahrani
- Faculty of Computer Science & Information Technology, Al Baha University, Al Baha, Saudi Arabia
| | - Muhammad Shafiq
- School of Information Engineering, Qujing Normal University, Sichuan, China
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6
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Uppamma P, Bhattacharya S. A multidomain bio-inspired feature extraction and selection model for diabetic retinopathy severity classification: an ensemble learning approach. Sci Rep 2023; 13:18572. [PMID: 37903967 PMCID: PMC10616283 DOI: 10.1038/s41598-023-45886-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 10/25/2023] [Indexed: 11/01/2023] Open
Abstract
Diabetes retinopathy (DR) is one of the leading causes of blindness globally. Early detection of this condition is essential for preventing patients' loss of eyesight caused by diabetes mellitus being untreated for an extended period. This paper proposes the design of an augmented bioinspired multidomain feature extraction and selection model for diabetic retinopathy severity estimation using an ensemble learning process. The proposed approach initiates by identifying DR severity levels from retinal images that segment the optical disc, macula, blood vessels, exudates, and hemorrhages using an adaptive thresholding process. Once the images are segmented, multidomain features are extracted from the retinal images, including frequency, entropy, cosine, gabor, and wavelet components. These data were fed into a novel Modified Moth Flame Optimization-based feature selection method that assisted in optimal feature selection. Finally, an ensemble model using various ML (machine learning) algorithms, which included Naive Bayes, K-Nearest Neighbours, Support Vector Machine, Multilayer Perceptron, Random Forests, and Logistic Regression were used to identify the various severity complications of DR. The experiments on different openly accessible data sources have shown that the proposed method outperformed conventional methods and achieved an Accuracy of 96.5% in identifying DR severity levels.
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Affiliation(s)
- Posham Uppamma
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, 632014, India
| | - Sweta Bhattacharya
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, 632014, India.
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7
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Nur Akkilic A, Sabir Z, Raja MAZ, Bulut H, Sadat R, Ali MR. Numerical performances through artificial neural networks for solving the vector-borne disease with lifelong immunity. Comput Methods Biomech Biomed Engin 2023; 26:1785-1795. [PMID: 36377246 DOI: 10.1080/10255842.2022.2145887] [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: 02/22/2022] [Accepted: 10/13/2022] [Indexed: 11/16/2022]
Abstract
The current study is related to solve a nonlinear vector-borne disease with a lifelong immunity model (VDLIM) by designing a computational stochastic framework using the strength of artificial Levenberg-Marquardt backpropagation neural network (ALMBNN). The detail of the nonlinear VDLIM is provided along with its five classes. The numerical performances of the results have been presented using the ALMBNN by taking three different cases to solve the nonlinear VDLIM using the training, sample data, testing and authentication. The selection of the statics is selected as 80% for training, while the data for both testing and validations is applied 10%. The results of the nonlinear VDLIM are performed using the ALMBNN and the correctness of the scheme is observed to compare the results with the reference solutions. The calculated performance of the results to solve the nonlinear VDLIM is applied for the reduction of the mean square error. In order to check the competence, efficacy, exactness and reliability of the ALMBNN, the numerical investigations using the proportional procedures based on the MSE, correlation, regression and error histograms are presented.
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Affiliation(s)
| | - Zulqurnain Sabir
- Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan
| | - Muhammad Asif Zahoor Raja
- Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin, Taiwan, R.O.C
| | - Hasan Bulut
- Department of Mathematics, Firat University, Elazığ, Turkey
| | - R Sadat
- Department of Mathematics, Zagazig Faculty of Engineering, Zagazig University, Zagazig, Egypt
| | - Mohamed R Ali
- Faculty of Engineering and Technology, Future University, Cairo, Egypt
- Department of Mathematics, Benha Faculty of Engineering, Benha University, Banha, Egypt
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8
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M G, K PL, Arumugam SR, N S. Conditional random field-recurrent neural network segmentation with optimized deep learning for brain tumour classification using magnetic resonance imaging. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2023.2178611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Affiliation(s)
- Geetha M
- Department of Computer Science and Engineering, Chennai Institute of Technology, Chennai, India
| | - Prasanna Lakshmi K
- Department of Information Technology, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India
| | - Sajeev Ram Arumugam
- Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India
| | - Sandhya N
- Department of CSE, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India
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9
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Iqbal S, Khan TM, Naveed K, Naqvi SS, Nawaz SJ. Recent trends and advances in fundus image analysis: A review. Comput Biol Med 2022; 151:106277. [PMID: 36370579 DOI: 10.1016/j.compbiomed.2022.106277] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/19/2022] [Accepted: 10/30/2022] [Indexed: 11/05/2022]
Abstract
Automated retinal image analysis holds prime significance in the accurate diagnosis of various critical eye diseases that include diabetic retinopathy (DR), age-related macular degeneration (AMD), atherosclerosis, and glaucoma. Manual diagnosis of retinal diseases by ophthalmologists takes time, effort, and financial resources, and is prone to error, in comparison to computer-aided diagnosis systems. In this context, robust classification and segmentation of retinal images are primary operations that aid clinicians in the early screening of patients to ensure the prevention and/or treatment of these diseases. This paper conducts an extensive review of the state-of-the-art methods for the detection and segmentation of retinal image features. Existing notable techniques for the detection of retinal features are categorized into essential groups and compared in depth. Additionally, a summary of quantifiable performance measures for various important stages of retinal image analysis, such as image acquisition and preprocessing, is provided. Finally, the widely used in the literature datasets for analyzing retinal images are described and their significance is emphasized.
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Affiliation(s)
- Shahzaib Iqbal
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan
| | - Tariq M Khan
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.
| | - Khuram Naveed
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan; Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark
| | - Syed S Naqvi
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan
| | - Syed Junaid Nawaz
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan
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10
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Amin J, Anjum MA, Malik M. Fused information of DeepLabv3+ and transfer learning model for semantic segmentation and rich features selection using equilibrium optimizer (EO) for classification of NPDR lesions. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108881] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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11
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Ghouali S, Onyema EM, Guellil MS, Wajid MA, Clare O, Cherifi W, Feham M. Artificial Intelligence-Based Teleopthalmology Application for Diagnosis of Diabetics Retinopathy. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2022; 3:124-133. [PMID: 36712318 PMCID: PMC9870271 DOI: 10.1109/ojemb.2022.3192780] [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: 05/22/2022] [Revised: 06/22/2022] [Accepted: 07/14/2022] [Indexed: 02/01/2023] Open
Abstract
Diabetic Retinopathy (DR) is one of the leading causes of blindness for people who have diabetes in the world. However, early detection of this disease can essentially decrease its effects on the patient. The recent breakthroughs in technologies, including the use of smart health systems based on Artificial intelligence, IoT and Blockchain are trying to improve the early diagnosis and treatment of diabetic retinopathy. In this study, we presented an AI-based smart teleopthalmology application for diagnosis of diabetic retinopathy. The app has the ability to facilitate the analyses of eye fundus images via deep learning from the Kaggle database using Tensor Flow mathematical library. The app would be useful in promoting mHealth and timely treatment of diabetic retinopathy by clinicians. With the AI-based application presented in this paper, patients can easily get supports and physicians and researchers can also mine or predict data on diabetic retinopathy and reports generated could assist doctors to determine the level of severity of the disease among the people.
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Affiliation(s)
- S Ghouali
- Faculty of Sciences and TechnologyMustapha Stambouli University Mascara 29000 Algeria
| | - E M Onyema
- Department of Mathematics and Computer ScienceCoal City University Enugu 400104 Nigeria
- Department of Mathematics and Computer ScienceCoal City University Enugu 400104 Nigeria
- Adjunct Faculty, Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai 602105 India
| | - M S Guellil
- Faculty of Economics, Business and Management Sciences, MCLDL LaboratoryUniversity of Mascara Mascara 29000 Algeria
| | - M A Wajid
- Department of Computer ScienceAligarh Muslim University Aligarh 202002 India
| | - O Clare
- Department of Mathematics and Computer ScienceCoal City University Enugu 400104 Nigeria
| | - W Cherifi
- InnoDev (Dev Software) Tlemcen 13000 Algeria
| | - M Feham
- STIC Lab, Faculty of TechnologyUniversity of Tlemcen Tlemcen 13000 Algeria
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12
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AI-Based Automatic Detection and Classification of Diabetic Retinopathy Using U-Net and Deep Learning. Symmetry (Basel) 2022. [DOI: 10.3390/sym14071427] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Artificial intelligence is widely applied to automate Diabetic retinopathy diagnosis. Diabetes-related retinal vascular disease is one of the world’s most common leading causes of blindness and vision impairment. Therefore, automated DR detection systems would greatly benefit the early screening and treatment of DR and prevent vision loss caused by it. Researchers have proposed several systems to detect abnormalities in retinal images in the past few years. However, Diabetic Retinopathy automatic detection methods have traditionally been based on hand-crafted feature extraction from the retinal images and using a classifier to obtain the final classification. DNN (Deep neural networks) have made several changes in the previous few years to assist overcome the problem mentioned above. We suggested a two-stage novel approach for automated DR classification in this research. Due to the low fraction of positive instances in the asymmetric Optic Disk (OD) and blood vessels (BV) detection system, preprocessing and data augmentation techniques are used to enhance the image quality and quantity. The first step uses two independent U-Net models for OD (optic disc) and BV (blood vessel) segmentation. In the second stage, the symmetric hybrid CNN-SVD model was created after preprocessing to extract and choose the most discriminant features following OD and BV extraction using Inception-V3 based on transfer learning, and detects DR by recognizing retinal biomarkers such as MA (microaneurysms), HM (hemorrhages), and exudates (EX). On EyePACS-1, Messidor-2, and DIARETDB0, the proposed methodology demonstrated state-of-the-art performance, with an average accuracy of 97.92%, 94.59%, and 93.52%, respectively. Extensive testing and comparisons with baseline approaches indicate the efficacy of the suggested methodology.
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13
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14
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Wang Z, Li X, Yao M, Li J, Jiang Q, Yan B. A new detection model of microaneurysms based on improved FC-DenseNet. Sci Rep 2022; 12:950. [PMID: 35046432 PMCID: PMC8770497 DOI: 10.1038/s41598-021-04750-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 12/28/2021] [Indexed: 02/06/2023] Open
Abstract
Diabetic retinopathy (DR) is a frequent vascular complication of diabetes mellitus and remains a leading cause of vision loss worldwide. Microaneurysm (MA) is usually the first symptom of DR that leads to blood leakage in the retina. Periodic detection of MAs will facilitate early detection of DR and reduction of vision injury. In this study, we proposed a novel model for the detection of MAs in fluorescein fundus angiography (FFA) images based on the improved FC-DenseNet, MAs-FC-DenseNet. FFA images were pre-processed by the Histogram Stretching and Gaussian Filtering algorithm to improve the quality of FFA images. Then, MA regions were detected by the improved FC-DenseNet. MAs-FC-DenseNet was compared against other FC-DenseNet models (FC-DenseNet56 and FC-DenseNet67) or the end-to-end models (DeeplabV3+ and PSPNet) to evaluate the detection performance of MAs. The result suggested that MAs-FC-DenseNet had higher values of evaluation metrics than other models, including pixel accuracy (PA), mean pixel accuracy (MPA), precision (Pre), recall (Re), F1-score (F1), and mean intersection over union (MIoU). Moreover, MA detection performance for MAs-FC-DenseNet was very close to the ground truth. Taken together, MAs-FC-DenseNet is a reliable model for rapid and accurate detection of MAs, which would be used for mass screening of DR patients.
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Affiliation(s)
- Zhenhua Wang
- College of Information Science, Shanghai Ocean University, Shanghai, 201306, China
| | - Xiaokai Li
- College of Information Science, Shanghai Ocean University, Shanghai, 201306, China
| | - Mudi Yao
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, 211166, China
| | - Jing Li
- The Affiliated Sixth People's Hospital, Shanghai Jiaotong University, Shanghai, 200233, China
| | - Qing Jiang
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, 211166, China.
| | - Biao Yan
- Eye Institute, Eye and ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, 200030, China.
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