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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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Xu X, Liu D, Huang G, Wang M, Lei M, Jia Y. Computer aided diagnosis of diabetic retinopathy based on multi-view joint learning. Comput Biol Med 2024; 174:108428. [PMID: 38631117 DOI: 10.1016/j.compbiomed.2024.108428] [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: 08/08/2023] [Revised: 04/02/2024] [Accepted: 04/04/2024] [Indexed: 04/19/2024]
Abstract
Diabetic retinopathy (DR) is a kind of ocular complication of diabetes, and its degree grade is an essential basis for early diagnosis of patients. Manual diagnosis is a long and expensive process with a specific risk of misdiagnosis. Computer-aided diagnosis can provide more accurate and practical treatment recommendations. In this paper, we propose a multi-view joint learning DR diagnostic model called RT2Net, which integrates the global features of fundus images and the local detailed features of vascular images to reduce the limitations of single fundus image learning. Firstly, the original image is preprocessed using operations such as contrast-limited adaptive histogram equalization, and the vascular structure of the extracted DR image is segmented. Then, the vascular image and fundus image are input into two branch networks of RT2Net for feature extraction, respectively, and the feature fusion module adaptively fuses the feature vectors' output from the branch networks. Finally, the optimized classification model is used to identify the five categories of DR. This paper conducts extensive experiments on the public datasets EyePACS and APTOS 2019 to demonstrate the method's effectiveness. The accuracy of RT2Net on the two datasets reaches 88.2% and 85.4%, and the area under the receiver operating characteristic curve (AUC) is 0.98 and 0.96, respectively. The excellent classification ability of RT2Net for DR can significantly help patients detect and treat lesions early and provide doctors with a more reliable diagnosis basis, which has significant clinical value for diagnosing DR.
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Affiliation(s)
- Xuebin Xu
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, Shaanxi, China; Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an 710121, Shaanxi, China; Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an 710121, Shaanxi, China.
| | - Dehua Liu
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, Shaanxi, China; Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an 710121, Shaanxi, China; Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an 710121, Shaanxi, China.
| | - Guohua Huang
- Weinan Central Hospital, Xi'an 714099, Shaanxi, China.
| | - Muyu Wang
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, Shaanxi, China; Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an 710121, Shaanxi, China; Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an 710121, Shaanxi, China.
| | - Meng Lei
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, Shaanxi, China; Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an 710121, Shaanxi, China; Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an 710121, Shaanxi, China.
| | - Yang Jia
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, Shaanxi, China; Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an 710121, Shaanxi, China; Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an 710121, Shaanxi, China.
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Anand V, Koundal D, Alghamdi WY, Alsharbi BM. Smart grading of diabetic retinopathy: an intelligent recommendation-based fine-tuned EfficientNetB0 framework. Front Artif Intell 2024; 7:1396160. [PMID: 38694880 PMCID: PMC11062181 DOI: 10.3389/frai.2024.1396160] [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: 03/05/2024] [Accepted: 03/27/2024] [Indexed: 05/04/2024] Open
Abstract
Diabetic retinopathy is a condition that affects the retina and causes vision loss due to blood vessel destruction. The retina is the layer of the eye responsible for visual processing and nerve signaling. Diabetic retinopathy causes vision loss, floaters, and sometimes blindness; however, it often shows no warning signals in the early stages. Deep learning-based techniques have emerged as viable options for automated illness classification as large-scale medical imaging datasets have become more widely available. To adapt to medical image analysis tasks, transfer learning makes use of pre-trained models to extract high-level characteristics from natural images. In this research, an intelligent recommendation-based fine-tuned EfficientNetB0 model has been proposed for quick and precise assessment for the diagnosis of diabetic retinopathy from fundus images, which will help ophthalmologists in early diagnosis and detection. The proposed EfficientNetB0 model is compared with three transfer learning-based models, namely, ResNet152, VGG16, and DenseNet169. The experimental work is carried out using publicly available datasets from Kaggle consisting of 3,200 fundus images. Out of all the transfer learning models, the EfficientNetB0 model has outperformed with an accuracy of 0.91, followed by DenseNet169 with an accuracy of 0.90. In comparison to other approaches, the proposed intelligent recommendation-based fine-tuned EfficientNetB0 approach delivers state-of-the-art performance on the accuracy, recall, precision, and F1-score criteria. The system aims to assist ophthalmologists in early detection, potentially alleviating the burden on healthcare units.
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Affiliation(s)
- Vatsala Anand
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
| | - Deepika Koundal
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
- Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam
| | - Wael Y. Alghamdi
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
| | - Bayan M. Alsharbi
- Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
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4
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Datta Rakshit R, Rattani A, Kisku DR. An LDOP approach for face identification under unconstrained scenarios. J EXP THEOR ARTIF IN 2023. [DOI: 10.1080/0952813x.2023.2183274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Affiliation(s)
- Rinku Datta Rakshit
- Department of Computer Engineering and Applications, GLA University, Mathura, India
| | - Ajita Rattani
- Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, Kansas, USA
| | - Dakshina Ranjan Kisku
- Department of Computer Science and Engineering, National Institute of Technology Durgapur, Durgapur, India
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Sebastian A, Elharrouss O, Al-Maadeed S, Almaadeed N. A Survey on Deep-Learning-Based Diabetic Retinopathy Classification. Diagnostics (Basel) 2023; 13:diagnostics13030345. [PMID: 36766451 PMCID: PMC9914068 DOI: 10.3390/diagnostics13030345] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 12/21/2022] [Accepted: 12/22/2022] [Indexed: 01/19/2023] Open
Abstract
The number of people who suffer from diabetes in the world has been considerably increasing recently. It affects people of all ages. People who have had diabetes for a long time are affected by a condition called Diabetic Retinopathy (DR), which damages the eyes. Automatic detection using new technologies for early detection can help avoid complications such as the loss of vision. Currently, with the development of Artificial Intelligence (AI) techniques, especially Deep Learning (DL), DL-based methods are widely preferred for developing DR detection systems. For this purpose, this study surveyed the existing literature on diabetic retinopathy diagnoses from fundus images using deep learning and provides a brief description of the current DL techniques that are used by researchers in this field. After that, this study lists some of the commonly used datasets. This is followed by a performance comparison of these reviewed methods with respect to some commonly used metrics in computer vision tasks.
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Selvachandran G, Quek SG, Paramesran R, Ding W, Son LH. Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods. Artif Intell Rev 2023; 56:915-964. [PMID: 35498558 PMCID: PMC9038999 DOI: 10.1007/s10462-022-10185-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2022] [Indexed: 02/02/2023]
Abstract
The exponential increase in the number of diabetics around the world has led to an equally large increase in the number of diabetic retinopathy (DR) cases which is one of the major complications caused by diabetes. Left unattended, DR worsens the vision and would lead to partial or complete blindness. As the number of diabetics continue to increase exponentially in the coming years, the number of qualified ophthalmologists need to increase in tandem in order to meet the demand for screening of the growing number of diabetic patients. This makes it pertinent to develop ways to automate the detection process of DR. A computer aided diagnosis system has the potential to significantly reduce the burden currently placed on the ophthalmologists. Hence, this review paper is presented with the aim of summarizing, classifying, and analyzing all the recent development on automated DR detection using fundus images from 2015 up to this date. Such work offers an unprecedentedly thorough review of all the recent works on DR, which will potentially increase the understanding of all the recent studies on automated DR detection, particularly on those that deploys machine learning algorithms. Firstly, in this paper, a comprehensive state-of-the-art review of the methods that have been introduced in the detection of DR is presented, with a focus on machine learning models such as convolutional neural networks (CNN) and artificial neural networks (ANN) and various hybrid models. Each AI will then be classified according to its type (e.g. CNN, ANN, SVM), its specific task(s) in performing DR detection. In particular, the models that deploy CNN will be further analyzed and classified according to some important properties of the respective CNN architectures of each model. A total of 150 research articles related to the aforementioned areas that were published in the recent 5 years have been utilized in this review to provide a comprehensive overview of the latest developments in the detection of DR. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-022-10185-6.
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Affiliation(s)
- Ganeshsree Selvachandran
- Department of Actuarial Science and Applied Statistics, Faculty of Business & Management, UCSI University, Jalan Menara Gading, Cheras, 56000 Kuala Lumpur, Malaysia
| | - Shio Gai Quek
- Department of Actuarial Science and Applied Statistics, Faculty of Business & Management, UCSI University, Jalan Menara Gading, Cheras, 56000 Kuala Lumpur, Malaysia
| | - Raveendran Paramesran
- Institute of Computer Science and Digital Innovation, UCSI University, Jalan Menara Gading, Cheras, 56000 Kuala Lumpur, Malaysia
| | - Weiping Ding
- School of Information Science and Technology, Nantong University, Nantong, 226019 People’s Republic of China
| | - Le Hoang Son
- VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam
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7
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Shaukat N, Amin J, Sharif M, Azam F, Kadry S, Krishnamoorthy S. Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning. J Pers Med 2022; 12:jpm12091454. [PMID: 36143239 PMCID: PMC9501488 DOI: 10.3390/jpm12091454] [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: 06/25/2022] [Revised: 08/18/2022] [Accepted: 08/20/2022] [Indexed: 11/23/2022] Open
Abstract
Diabetic retinopathy (DR) is a drastic disease. DR embarks on vision impairment when it is left undetected. In this article, learning-based techniques are presented for the segmentation and classification of DR lesions. The pre-trained Xception model is utilized for deep feature extraction in the segmentation phase. The extracted features are fed to Deeplabv3 for semantic segmentation. For the training of the segmentation model, an experiment is performed for the selection of the optimal hyperparameters that provided effective segmentation results in the testing phase. The multi-classification model is developed for feature extraction using the fully connected (FC) MatMul layer of efficient-net-b0 and pool-10 of the squeeze-net. The extracted features from both models are fused serially, having the dimension of N × 2020, amidst the best N × 1032 features chosen by applying the marine predictor algorithm (MPA). The multi-classification of the DR lesions into grades 0, 1, 2, and 3 is performed using neural network and KNN classifiers. The proposed method performance is validated on open access datasets such as DIARETDB1, e-ophtha-EX, IDRiD, and Messidor. The obtained results are better compared to those of the latest published works.
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Affiliation(s)
- Natasha Shaukat
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan
| | - Javeria Amin
- Department of Computer Science, University of Wah, Wah Campus, Wah Cantt 47010, Pakistan
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan
- Correspondence: (M.S.); (S.K.)
| | - Faisal Azam
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
| | - Sujatha Krishnamoorthy
- Zhejiang Bioinformatics International Science and Technology Cooperation Center, Wenzhou-Kean University, Wenzhou 325060, China
- Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics, Wenzhou-Kean University, Wenzhou 325060, China
- Correspondence: (M.S.); (S.K.)
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8
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Naseem A, Habib R, Naz T, Atif M, Arif M, Allaoua Chelloug S. Novel Internet of Things based approach toward diabetes prediction using deep learning models. Front Public Health 2022; 10:914106. [PMID: 36091536 PMCID: PMC9449533 DOI: 10.3389/fpubh.2022.914106] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 08/03/2022] [Indexed: 01/22/2023] Open
Abstract
The integration of the Internet of Things with machine learning in different disciplines has benefited from recent technological advancements. In medical IoT, the fusion of these two disciplines can be extremely beneficial as it allows the creation of a receptive and interconnected environment and offers a variety of services to medical professionals and patients. Doctors can make early decisions to save a patient's life when disease forecasts are made early. IoT sensor captures the data from the patients, and machine learning techniques are used to analyze the data and predict the presence of the fatal disease i.e., diabetes. The goal of this research is to make a smart patient's health monitoring system based on machine learning that helps to detect the presence of a chronic disease in patient early and accurately. For the implementation, the diabetic dataset has been used. In order to detect the presence of the fatal disease, six different machine learning techniques are used i.e., Support Vector Machine (SVM), Logistic Regression, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). The performance of the proposed model is evaluated by using four evaluation metrics i.e., accuracy, precision, recall, and F1-Score. The RNN outperformed remaining algorithms in terms of accuracy (81%), precision (75%), and F1-Score (65%). However, the recall (56%) for ANN was higher as compared to SVM and logistic regression, CNN, RNN, and LSTM. With the help of this proposed patient's health monitoring system, doctors will be able to diagnose the presence of the disease earlier.
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Affiliation(s)
- Anum Naseem
- Faculty of Computer Sciences, Ibadat International University, Islamabad, Pakistan
| | - Raja Habib
- Faculty of Computer Sciences, Ibadat International University, Islamabad, Pakistan
| | - Tabbasum Naz
- CMAC Future Manufacturing Research Hub, University of Strathclyde, Glasgow, United Kingdom
| | - Muhammad Atif
- Department of Computer Science and IT, The University of Lahore, Lahore, Pakistan
| | - Muhammad Arif
- Department of Computer Science and IT, The University of Lahore, Lahore, Pakistan,*Correspondence: Muhammad Arif
| | - Samia Allaoua Chelloug
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia,Samia Allaoua Chelloug
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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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10
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Retinal Vascularization Abnormalities Studied by Optical Coherence Tomography Angiography (OCTA) in Type 2 Diabetic Patients with Moderate Diabetic Retinopathy. Diagnostics (Basel) 2022; 12:diagnostics12020379. [PMID: 35204470 PMCID: PMC8871460 DOI: 10.3390/diagnostics12020379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/18/2022] [Accepted: 01/29/2022] [Indexed: 11/17/2022] Open
Abstract
Diabetic retinopathy (DR) is the most severe and frequent retinal vascular disease that causes significant visual loss on a global scale. The purpose of our study was to evaluate retinal vascularization in the superficial capillary plexus (SCP), the deep capillary plexus (DCP) and the choriocapillaris (CC) and changes in the foveal avascular zone (FAZ) by optical tomography angiography (OCTA) in patients with type 2 diabetes mellitus (DM2) with moderate DR but without diabetic macular oedema (DME). Fifty-four eyes of DM2 with moderate DR (level 43 in the ETDRS scale) and without DME and 73 age-matched healthy eyes were evaluated using OCTA with swept-source (SS)-OCT to measure microvascularization changes in SCP, DCP, CC and the FAZ. The mean ages were 64.06 ± 11.98 and 60.79 ± 8.62 years in the DM2 and control groups, respectively. Visual acuity (VA) was lower in the DM2 patients (p = 0.001), OCTA showed changes in the SCP with a significant diminution in the vascular density and the FAZ area was significantly higher compared to healthy controls, with p < 0.001 at the SCP level. The most prevalent anatomical alterations were peripheral disruption in the SCP (83.3%), microaneurysms (MA) in the SCP and in the DCP (79.6% and 79.6%, respectively) and flow changes in the DCP (81.5%). A significant positive correlation was observed between the DM2 duration and the FAZ area in the SCP (0.304 with p = 0.025). A significant negative correlation was also found between age and CC central perfusion (p < 0.001). In summary, a decrease in the vascular density in DM2 patients with moderate DR without DME was observed, especially at the retinal SPC level. Furthermore, it was found that the FAZ was increased in the DM2 group in both retinal plexuses and was greater in the SCP group.
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11
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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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Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:8459-8486. [PMID: 35039756 PMCID: PMC8754556 DOI: 10.1007/s12652-021-03612-z] [Citation(s) in RCA: 147] [Impact Index Per Article: 73.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 11/18/2021] [Indexed: 05/03/2023]
Abstract
Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.
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Affiliation(s)
- Yogesh Kumar
- Department of Computer Engineering, Indus Institute of Technology and Engineering, Indus University, Ahmedabad, 382115 India
| | | | - Ruchi Singla
- Department of Research, Innovations, Sponsored Projects and Entrepreneurship, CGC Landran, Mohali, India
| | - Muhammad Fazal Ijaz
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006 South Korea
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Optical Coherence Tomography Angiography in Diabetic Patients: A Systematic Review. Biomedicines 2021; 10:biomedicines10010088. [PMID: 35052768 PMCID: PMC8773551 DOI: 10.3390/biomedicines10010088] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 12/28/2021] [Accepted: 12/29/2021] [Indexed: 01/20/2023] Open
Abstract
Background: Diabetic retinopathy (DR) is the leading cause of legal blindness in the working population in developed countries. Optical coherence tomography (OCT) angiography (OCTA) has risen as an essential tool in the diagnosis and control of diabetic patients, with and without DR, allowing visualisation of the retinal and choroidal microvasculature, their qualitative and quantitative changes, the progression of vascular disease, quantification of ischaemic areas, and the detection of preclinical changes. The aim of this article is to analyse the current applications of OCTA and provide an updated overview of them in the evaluation of DR. Methods: A systematic literature search was performed in PubMed and Embase, including the keywords “OCTA” OR “OCT angiography” OR “optical coherence tomography angiography” AND “diabetes” OR “diabetes mellitus” OR “diabetic retinopathy” OR “diabetic maculopathy” OR “diabetic macular oedema” OR “diabetic macular ischaemia”. Of the 1456 studies initially identified, 107 studies were screened after duplication, and those articles that did not meet the selection criteria were removed. Finally, after looking for missing data, we included 135 studies in this review. Results: We present the common and distinctive findings in the analysed papers after the literature search including the diagnostic use of OCTA in diabetes mellitus (DM) patients. We describe previous findings in retinal vascularization, including microaneurysms, foveal avascular zone (FAZ) changes in both size and morphology, changes in vascular perfusion, the appearance of retinal microvascular abnormalities or new vessels, and diabetic macular oedema (DME) and the use of deep learning technology applied to this disease. Conclusion: OCTA findings enable the diagnosis and follow-up of DM patients, including those with no detectable lesions with other devices. The evaluation of retinal and choroidal plexuses using OCTA is a fundamental tool for the diagnosis and prognosis of DR.
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Wu J, Hu R, Xiao Z, Chen J, Liu J. Vision Transformer-based recognition of diabetic retinopathy grade. Med Phys 2021; 48:7850-7863. [PMID: 34693536 DOI: 10.1002/mp.15312] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 10/14/2021] [Accepted: 10/15/2021] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND In the domain of natural language processing, Transformers are recognized as state-of-the-art models, which opposing to typical convolutional neural networks (CNNs) do not rely on convolution layers. Instead, Transformers employ multi-head attention mechanisms as the main building block to capture long-range contextual relations between image pixels. Recently, CNNs dominated the deep learning solutions for diabetic retinopathy grade recognition. However, spurred by the advantages of Transformers, we propose a Transformer-based method that is appropriate for recognizing the grade of diabetic retinopathy. PURPOSE The purposes of this work are to demonstrate that (i) the pure attention mechanism is suitable for diabetic retinopathy grade recognition and (ii) Transformers can replace traditional CNNs for diabetic retinopathy grade recognition. METHODS This paper proposes a Vision Transformer-based method to recognize the grade of diabetic retinopathy. Fundus images are subdivided into non-overlapping patches, which are then converted into sequences by flattening, and undergo a linear and positional embedding process to preserve positional information. Then, the generated sequence is input into several multi-head attention layers to generate the final representation. The first token sequence is input to a softmax classification layer to produce the recognition output in the classification stage. RESULTS The dataset for training and testing employs fundus images of different resolutions, subdivided into patches. We challenge our method against current CNNs and extreme learning machines and achieve an appealing performance. Specifically, the suggested deep learning architecture attains an accuracy of 91.4%, specificity = 0.977 (95% confidence interval (CI) (0.951-1)), precision = 0.928 (95% CI (0.852-1)), sensitivity = 0.926 (95% CI (0.863-0.989)), quadratic weighted kappa score = 0.935, and area under curve (AUC) = 0.986. CONCLUSION Our comparative experiments against current methods conclude that our model is competitive and highlight that an attention mechanism based on a Vision Transformer model is promising for the diabetic retinopathy grade recognition task.
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Affiliation(s)
- Jianfang Wu
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Ruo Hu
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Zhenghong Xiao
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Jiaxu Chen
- School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
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15
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Wu JH, Liu TYA, Hsu WT, Ho JHC, Lee CC. Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis. J Med Internet Res 2021; 23:e23863. [PMID: 34407500 PMCID: PMC8406115 DOI: 10.2196/23863] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 11/19/2020] [Accepted: 04/30/2021] [Indexed: 12/23/2022] Open
Abstract
Background Diabetic retinopathy (DR), whose standard diagnosis is performed by human experts, has high prevalence and requires a more efficient screening method. Although machine learning (ML)–based automated DR diagnosis has gained attention due to recent approval of IDx-DR, performance of this tool has not been examined systematically, and the best ML technique for use in a real-world setting has not been discussed. Objective The aim of this study was to systematically examine the overall diagnostic accuracy of ML in diagnosing DR of different categories based on color fundus photographs and to determine the state-of-the-art ML approach. Methods Published studies in PubMed and EMBASE were searched from inception to June 2020. Studies were screened for relevant outcomes, publication types, and data sufficiency, and a total of 60 out of 2128 (2.82%) studies were retrieved after study selection. Extraction of data was performed by 2 authors according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), and the quality assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Meta-analysis of diagnostic accuracy was pooled using a bivariate random effects model. The main outcomes included diagnostic accuracy, sensitivity, and specificity of ML in diagnosing DR based on color fundus photographs, as well as the performances of different major types of ML algorithms. Results The primary meta-analysis included 60 color fundus photograph studies (445,175 interpretations). Overall, ML demonstrated high accuracy in diagnosing DR of various categories, with a pooled area under the receiver operating characteristic (AUROC) ranging from 0.97 (95% CI 0.96-0.99) to 0.99 (95% CI 0.98-1.00). The performance of ML in detecting more-than-mild DR was robust (sensitivity 0.95; AUROC 0.97), and by subgroup analyses, we observed that robust performance of ML was not limited to benchmark data sets (sensitivity 0.92; AUROC 0.96) but could be generalized to images collected in clinical practice (sensitivity 0.97; AUROC 0.97). Neural network was the most widely used method, and the subgroup analysis revealed a pooled AUROC of 0.98 (95% CI 0.96-0.99) for studies that used neural networks to diagnose more-than-mild DR. Conclusions This meta-analysis demonstrated high diagnostic accuracy of ML algorithms in detecting DR on color fundus photographs, suggesting that state-of-the-art, ML-based DR screening algorithms are likely ready for clinical applications. However, a significant portion of the earlier published studies had methodology flaws, such as the lack of external validation and presence of spectrum bias. The results of these studies should be interpreted with caution.
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Affiliation(s)
- Jo-Hsuan Wu
- Shiley Eye Institute and Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, CA, United States
| | - T Y Alvin Liu
- Retina Division, Wilmer Eye Institute, The Johns Hopkins Medicine, Baltimore, MD, United States
| | - Wan-Ting Hsu
- Harvard TH Chan School of Public Health, Boston, MA, United States
| | | | - Chien-Chang Lee
- Health Data Science Research Group, National Taiwan University Hospital, Taipei, Taiwan.,The Centre for Intelligent Healthcare, National Taiwan University Hospital, Taipei, Taiwan.,Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan
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16
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Dairi A, Harrou F, Zeroual A, Hittawe MM, Sun Y. Comparative study of machine learning methods for COVID-19 transmission forecasting. J Biomed Inform 2021; 118:103791. [PMID: 33915272 PMCID: PMC8074522 DOI: 10.1016/j.jbi.2021.103791] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 03/17/2021] [Accepted: 04/05/2021] [Indexed: 12/16/2022]
Abstract
Within the recent pandemic, scientists and clinicians are engaged in seeking new technology to stop or slow down the COVID-19 pandemic. The benefit of machine learning, as an essential aspect of artificial intelligence, on past epidemics offers a new line to tackle the novel Coronavirus outbreak. Accurate short-term forecasting of COVID-19 spread plays an essential role in improving the management of the overcrowding problem in hospitals and enables appropriate optimization of the available resources (i.e., materials and staff).This paper presents a comparative study of machine learning methods for COVID-19 transmission forecasting. We investigated the performances of deep learning methods, including the hybrid convolutional neural networks-Long short-term memory (LSTM-CNN), the hybrid gated recurrent unit-convolutional neural networks (GAN-GRU), GAN, CNN, LSTM, and Restricted Boltzmann Machine (RBM), as well as baseline machine learning methods, namely logistic regression (LR) and support vector regression (SVR). The employment of hybrid models (i.e., LSTM-CNN and GAN-GRU) is expected to eventually improve the forecasting accuracy of COVID-19 future trends. The performance of the investigated deep learning and machine learning models was tested using confirmed and recovered COVID-19 cases time-series data from seven impacted countries: Brazil, France, India, Mexico, Russia, Saudi Arabia, and the US. The results reveal that hybrid deep learning models can efficiently forecast COVID-19 cases. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models. Furthermore, results showed that LSTM-CNN achieved improved performances with an averaged mean absolute percentage error of 3.718%, among others.
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Affiliation(s)
- Abdelkader Dairi
- University of Science and Technology of Oran-Mohamed Boudiaf (USTO-MB), Computer Science department Signal, Image and Speech Laboratory (SIMPA) Laboratory, El Mnaouar, BP 1505, Bir El Djir 31000, Oran, Algeria.
| | - Fouzi Harrou
- King Abdullah University of Science and Technology (KAUST) Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia.
| | - Abdelhafid Zeroual
- Faculty of Technology, Department of electrical engineering, University of 20 August 1955, Skikda 21000, Algeria; LAIG Laboratory, University of 08 May 1945, Guelma 24000, Algeria
| | - Mohamad Mazen Hittawe
- King Abdullah University of Science and Technology (KAUST) Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia
| | - Ying Sun
- King Abdullah University of Science and Technology (KAUST) Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia
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Govindarajan S, Swaminathan R. Extreme Learning Machine based Differentiation of Pulmonary Tuberculosis in Chest Radiographs using Integrated Local Feature Descriptors. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 204:106058. [PMID: 33789212 DOI: 10.1016/j.cmpb.2021.106058] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 03/16/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Computer aided diagnostics of Pulmonary Tuberculosis in chest radiographs relies on the differentiation of subtle and non-specific alterations in the images. In this study, an attempt has been made to identify and classify Tuberculosis conditions from healthy subjects in chest radiographs using integrated local feature descriptors and variants of extreme learning machine. METHODS Lung fields in the chest images are segmented using Reaction Diffusion Level Set method. Local feature descriptors such as Median Robust Extended Local Binary Patterns and Gradient Local Ternary Patterns are extracted. Extreme Learning Machine (ELM) and Online Sequential ELM (OSELM) classifiers are employed to identify Tuberculosis conditions and, their performances are analysed using standard metrics. RESULTS Results show that the adopted segmentation method is able to delineate lung fields in both healthy and Tuberculosis images. Extracted features are statistically significant even in images with inter and intra subject variability. Sigmoid activation function yields accuracy and sensitivity values greater than 98% for both the classifiers. Highest sensitivity is observed with OSELM for minimal significant features in detecting Tuberculosis images. CONCLUSION As ELM based method is able to differentiate the subtle changes in inter and intra subject variations of chest X-ray images, the proposed methodology seems to be useful for computer-based detection of Pulmonary Tuberculosis.
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Affiliation(s)
- Satyavratan Govindarajan
- Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India.
| | - Ramakrishnan Swaminathan
- Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India
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18
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Ramasamy LK, Padinjappurathu SG, Kadry S, Damaševičius R. Detection of diabetic retinopathy using a fusion of textural and ridgelet features of retinal images and sequential minimal optimization classifier. PeerJ Comput Sci 2021; 7:e456. [PMID: 34013026 PMCID: PMC8114804 DOI: 10.7717/peerj-cs.456] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 03/03/2021] [Indexed: 06/12/2023]
Abstract
Diabetes is one of the most prevalent diseases in the world, which is a metabolic disorder characterized by high blood sugar. Diabetes complications are leading to Diabetic Retinopathy (DR). The early stages of DR may have either no sign or cause minor vision problems, but later stages of the disease can lead to blindness. DR diagnosis is an exceedingly difficult task because of changes in the retina during the disease stages. An automatic DR early detection method can save a patient's vision and can also support the ophthalmologists in DR screening. This paper develops a model for the diagnostics of DR. Initially, we extract and fuse the ophthalmoscopic features from the retina images based on textural gray-level features like co-occurrence, run-length matrix, as well as the coefficients of the Ridgelet Transform. Based on the retina features, the Sequential Minimal Optimization (SMO) classification is used to classify diabetic retinopathy. For performance analysis, the openly accessible retinal image datasets are used, and the findings of the experiments demonstrate the quality and efficacy of the proposed method (we achieved 98.87% sensitivity, 95.24% specificity, 97.05% accuracy on DIARETDB1 dataset, and 90.9% sensitivity, 91.0% specificity, 91.0% accuracy on KAGGLE dataset).
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19
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Nagasawa T, Tabuchi H, Masumoto H, Morita S, Niki M, Ohara Z, Yoshizumi Y, Mitamura Y. Accuracy of Diabetic Retinopathy Staging with a Deep Convolutional Neural Network Using Ultra-Wide-Field Fundus Ophthalmoscopy and Optical Coherence Tomography Angiography. J Ophthalmol 2021; 2021:6651175. [PMID: 33884202 PMCID: PMC8041547 DOI: 10.1155/2021/6651175] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 03/11/2021] [Accepted: 03/26/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE The present study aimed to compare the accuracy of diabetic retinopathy (DR) staging with a deep convolutional neural network (DCNN) using two different types of fundus cameras and composite images. METHOD The study included 491 ultra-wide-field fundus ophthalmoscopy and optical coherence tomography angiography (OCTA) images that passed an image-quality review and were graded as no apparent DR (NDR; 169 images), mild nonproliferative DR (NPDR; 76 images), moderate NPDR (54 images), severe NPDR (90 images), and proliferative DR (PDR; 102 images) by three retinal experts by the International Clinical Diabetic Retinopathy Severity Scale. The findings of tests 1 and 2 to identify no apparent diabetic retinopathy (NDR) and PDR, respectively, were then assessed. For each verification, Optos, OCTA, and Optos OCTA imaging scans with DCNN were performed. RESULT The Optos, OCTA, and Optos OCTA imaging test results for comparison between NDR and DR showed mean areas under the curve (AUC) of 0.79, 0.883, and 0.847; sensitivity rates of 80.9%, 83.9%, and 78.6%; and specificity rates of 55%, 71.6%, and 69.8%, respectively. Meanwhile, the Optos, OCTA, and Optos OCTA imaging test results for comparison between NDR and PDR showed mean AUC of 0.981, 0.928, and 0.964; sensitivity rates of 90.2%, 74.5%, and 80.4%; and specificity rates of 97%, 97%, and 96.4%, respectively. CONCLUSION The combination of Optos and OCTA imaging with DCNN could detect DR at desirable levels of accuracy and may be useful in clinical practice and retinal screening. Although the combination of multiple imaging techniques might overcome their individual weaknesses and provide comprehensive imaging, artificial intelligence in classifying multimodal images has not always produced accurate results.
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Affiliation(s)
- Toshihiko Nagasawa
- Department of Ophthalmology, Saneikai Tsukazaki Hospital, Himeji 671-1227, Japan
| | - Hitoshi Tabuchi
- Department of Ophthalmology, Saneikai Tsukazaki Hospital, Himeji 671-1227, Japan
- Department of Technology and Design Thinking for Medicine, Hiroshima University, Hiroshima 739-8511, Japan
| | - Hiroki Masumoto
- Department of Ophthalmology, Saneikai Tsukazaki Hospital, Himeji 671-1227, Japan
| | - Shoji Morita
- Graduate School of Engineering, University of Hyogo, Kobe 657-0013, Japan
| | - Masanori Niki
- Department of Ophthalmology, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima 770-8851, Japan
| | - Zaigen Ohara
- Department of Ophthalmology, Saneikai Tsukazaki Hospital, Himeji 671-1227, Japan
| | - Yuki Yoshizumi
- Department of Ophthalmology, Saneikai Tsukazaki Hospital, Himeji 671-1227, Japan
| | - Yoshinori Mitamura
- Department of Ophthalmology, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima 770-8851, Japan
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NAIR ARUNT, MUTHUVEL K. AUTOMATED SCREENING OF DIABETIC RETINOPATHY WITH OPTIMIZED DEEP CONVOLUTIONAL NEURAL NETWORK: ENHANCED MOTH FLAME MODEL. J MECH MED BIOL 2021. [DOI: 10.1142/s0219519421500056] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Nowadays, analysis on retinal image exists as one of the challenging area for study. Numerous retinal diseases could be recognized by analyzing the variations taking place in retina. However, the main disadvantage among those studies is that, they do not have higher recognition accuracy. The proposed framework includes four phases namely, (i) Blood Vessel Segmentation (ii) Feature Extraction (iii) Optimal Feature Selection and (iv) Classification. Initially, the input fundus image is subjected to blood vessel segmentation from which two binary thresholded images (one from High Pass Filter (HPF) and other from top-hat reconstruction) are acquired. These two images are differentiated and the areas that are common to both are said to be the major vessels and the left over regions are fused to form vessel sub-image. These vessel sub-images are classified with Gaussian Mixture Model (GMM) classifier and the resultant is summed up with the major vessels to form the segmented blood vessels. The segmented images are subjected to feature extraction process, where the features like proposed Local Binary Pattern (LBP), Gray-Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRM) are extracted. As the curse of dimensionality seems to be the greatest issue, it is important to select the appropriate features from the extracted one for classification. In this paper, a new improved optimization algorithm Moth Flame with New Distance Formulation (MF-NDF) is introduced for selecting the optimal features. Finally, the selected optimal features are subjected to Deep Convolutional Neural Network (DCNN) model for classification. Further, in order to make the precise diagnosis, the weights of DCNN are optimally tuned by the same optimization algorithm. The performance of the proposed algorithm will be compared against the conventional algorithms in terms of positive and negative measures.
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Affiliation(s)
- ARUN T NAIR
- Department of Electrical and Electronics Engineering, Noorul Islam Centre for Higher Education, Kumaracoil 629180, Tamil Nadu, India
| | - K. MUTHUVEL
- Department of Electrical and Electronics Engineering, Noorul Islam Centre for Higher Education, Kumaracoil 629180, Tamil Nadu, India
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21
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Qummar S, Khan FG, Shah S, Khan A, Din A, Gao J. Deep Learning Techniques for Diabetic Retinopathy Detection. Curr Med Imaging 2021; 16:1201-1213. [DOI: 10.2174/1573405616666200213114026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 11/26/2019] [Accepted: 12/19/2019] [Indexed: 11/22/2022]
Abstract
Diabetes occurs due to the excess of glucose in the blood that may affect many organs
of the body. Elevated blood sugar in the body causes many problems including Diabetic Retinopathy
(DR). DR occurs due to the mutilation of the blood vessels in the retina. The manual detection
of DR by ophthalmologists is complicated and time-consuming. Therefore, automatic detection is
required, and recently different machine and deep learning techniques have been applied to detect
and classify DR. In this paper, we conducted a study of the various techniques available in the literature
for the identification/classification of DR, the strengths and weaknesses of available datasets
for each method, and provides the future directions. Moreover, we also discussed the different
steps of detection, that are: segmentation of blood vessels in a retina, detection of lesions, and other
abnormalities of DR.
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Affiliation(s)
- Sehrish Qummar
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan
| | - Fiaz Gul Khan
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan
| | - Sajid Shah
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan
| | - Ahmad Khan
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan
| | - Ahmad Din
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan
| | - Jinfeng Gao
- Department of Information Engineering, Huanghuai University, Henan, China
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22
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Mohanty A, Fatrekar AP, Krishnan S, Vernekar AA. A concise discussion on the potential spectral tools for the rapid COVID-19 detection. RESULTS IN CHEMISTRY 2021; 3:100138. [PMID: 33972921 PMCID: PMC8099787 DOI: 10.1016/j.rechem.2021.100138] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 05/04/2021] [Indexed: 12/28/2022] Open
Abstract
Developing robust methods to detect the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a causative agent for the current global health pandemic, is an exciting area of research. Nevertheless, the currently used conventional reverse transcription-polymerase chain reaction (RT-PCR) technique in COVID-19 detection endures with some inevitable limitations. Consequently, the establishment of rapid diagnostic tools and quick isolation of infected patients is highly essential. Furthermore, the requirement of point-of-care testing is the need of the hour. Considering this, we have provided a brief review of the use of very recently reported robust spectral tools for rapid COVID-19 detection. The spectral tools include, colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) and matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), with the admittance of principal component analysis (PCA) and machine learning (ML) for meeting the high-throughput and fool-proof platforms for the detection of SARS-CoV-2, are reviewed. Recently, these techniques have been readily applied to screen a large number of suspected patients within a short period and they demonstrated higher sensitivity for the detection of COVID-19 patients from unaffected human subjects.
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Affiliation(s)
- Abhijeet Mohanty
- Inorganic and Physical Chemistry Laboratory, Council of Scientific and Industrial Research (CSIR)-Central Leather Research Institute (CLRI), Chennai 600020, India
- Academy of Scientific and Innovative Research (AcSIR), New Delhi, India
| | - Adarsh P Fatrekar
- Inorganic and Physical Chemistry Laboratory, Council of Scientific and Industrial Research (CSIR)-Central Leather Research Institute (CLRI), Chennai 600020, India
- Academy of Scientific and Innovative Research (AcSIR), New Delhi, India
| | | | - Amit A Vernekar
- Inorganic and Physical Chemistry Laboratory, Council of Scientific and Industrial Research (CSIR)-Central Leather Research Institute (CLRI), Chennai 600020, India
- Academy of Scientific and Innovative Research (AcSIR), New Delhi, India
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23
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Optical coherence tomography angiography in diabetic retinopathy: an updated review. Eye (Lond) 2020; 35:149-161. [PMID: 33099579 DOI: 10.1038/s41433-020-01233-y] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 09/27/2020] [Accepted: 10/15/2020] [Indexed: 12/17/2022] Open
Abstract
Diabetic retinopathy (DR) is a common microvascular complication of diabetes mellitus. Optical coherence tomography angiography (OCTA) has been developed to visualize the retinal microvasculature and choriocapillaris based on the motion contrast of circulating blood cells. Depth-resolved ability and non-invasive nature of OCTA allow for repeated examinations and visualization of microvasculature at the retinal capillary plexuses and choriocapillaris. OCTA enables quantification of microvascular alterations in the retinal capillary network, in addition to the detection of classical features associated with DR, including microaneurysms, intraretinal microvascular abnormalities, and neovascularization. OCTA has a promising role as an objective tool for quantifying extent of microvascular damage and identify eyes with diabetic macular ischaemia contributed to visual loss. Furthermore, OCTA can identify preclinical microvascular abnormalities preceding the onset of clinically detectable DR. In this review, we focused on the applications of OCTA derived quantitative metrics that are relevant to early detection, staging and progression of DR. Advancement of OCTA technology in clinical research will ultimately lead to enhancement of individualised management of DR and prevention of visual impairment in patients with diabetes.
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Lalmuanawma S, Hussain J, Chhakchhuak L. Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. CHAOS, SOLITONS, AND FRACTALS 2020; 139:110059. [PMID: 32834612 PMCID: PMC7315944 DOI: 10.1016/j.chaos.2020.110059] [Citation(s) in RCA: 289] [Impact Index Per Article: 72.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 06/23/2020] [Indexed: 05/17/2023]
Abstract
BACKGROUND AND OBJECTIVE During the recent global urgency, scientists, clinicians, and healthcare experts around the globe keep on searching for a new technology to support in tackling the Covid-19 pandemic. The evidence of Machine Learning (ML) and Artificial Intelligence (AI) application on the previous epidemic encourage researchers by giving a new angle to fight against the novel Coronavirus outbreak. This paper aims to comprehensively review the role of AI and ML as one significant method in the arena of screening, predicting, forecasting, contact tracing, and drug development for SARS-CoV-2 and its related epidemic. METHOD A selective assessment of information on the research article was executed on the databases related to the application of ML and AI technology on Covid-19. Rapid and critical analysis of the three crucial parameters, i.e., abstract, methodology, and the conclusion was done to relate to the model's possibilities for tackling the SARS-CoV-2 epidemic. RESULT This paper addresses on recent studies that apply ML and AI technology towards augmenting the researchers on multiple angles. It also addresses a few errors and challenges while using such algorithms in real-world problems. The paper also discusses suggestions conveying researchers on model design, medical experts, and policymakers in the current situation while tackling the Covid-19 pandemic and ahead. CONCLUSION The ongoing development in AI and ML has significantly improved treatment, medication, screening, prediction, forecasting, contact tracing, and drug/vaccine development process for the Covid-19 pandemic and reduce the human intervention in medical practice. However, most of the models are not deployed enough to show their real-world operation, but they are still up to the mark to tackle the SARS-CoV-2 epidemic.
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Affiliation(s)
- Samuel Lalmuanawma
- Department of Mathematics & Computer Science, Mizoram University, Tanhril, Aizawl, Mizoram, 796004, India
| | - Jamal Hussain
- Department of Mathematics & Computer Science, Mizoram University, Tanhril, Aizawl, Mizoram, 796004, India
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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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Machine learning and artificial intelligence based Diabetes Mellitus detection and self-management: A systematic review. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2020. [DOI: 10.1016/j.jksuci.2020.06.013] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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