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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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Zhao T, Guan Y, Tu D, Yuan L, Lu G. Neighbored-attention U-net (NAU-net) for diabetic retinopathy image segmentation. Front Med (Lausanne) 2023; 10:1309795. [PMID: 38131040 PMCID: PMC10733532 DOI: 10.3389/fmed.2023.1309795] [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: 10/08/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
Background Diabetic retinopathy-related (DR-related) diseases are posing an increasing threat to eye health as the number of patients with diabetes mellitus that are young increases significantly. The automatic diagnosis of DR-related diseases has benefited from the rapid development of image semantic segmentation and other deep learning technology. Methods Inspired by the architecture of U-Net family, a neighbored attention U-Net (NAU-Net) is designed to balance the identification performance and computational cost for DR fundus image segmentation. In the new network, only the neighboring high- and low-dimensional feature maps of the encoder and decoder are fused by using four attention gates. With the help of this improvement, the common target features in the high-dimensional feature maps of encoder are enhanced, and they are also fused with the low-dimensional feature map of decoder. Moreover, this network fuses only neighboring layers and does not include the inner layers commonly used in U-Net++. Consequently, the proposed network incurs a better identification performance with a lower computational cost. Results The experimental results of three open datasets of DR fundus images, including DRIVE, HRF, and CHASEDB, indicate that the NAU-Net outperforms FCN, SegNet, attention U-Net, and U-Net++ in terms of Dice score, IoU, accuracy, and precision, while its computation cost is between attention U-Net and U-Net++. Conclusion The proposed NAU-Net exhibits better performance at a relatively low computational cost and provides an efficient novel approach for DR fundus image segmentation and a new automatic tool for DR-related eye disease diagnosis.
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Affiliation(s)
- Tingting Zhao
- The Second Department of Internal Medicine, Donghu Hospital of Wuhan, Wuhan, China
| | - Yawen Guan
- The Second Department of Internal Medicine, Donghu Hospital of Wuhan, Wuhan, China
| | - Dan Tu
- The Second Department of Internal Medicine, Donghu Hospital of Wuhan, Wuhan, China
| | - Lixia Yuan
- The Department of Ophthalmology, Donghu Hospital of Wuhan, Wuhan, China
| | - Guangtao Lu
- Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan, China
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Wahab Sait AR. A Lightweight Diabetic Retinopathy Detection Model Using a Deep-Learning Technique. Diagnostics (Basel) 2023; 13:3120. [PMID: 37835861 PMCID: PMC10572365 DOI: 10.3390/diagnostics13193120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 10/15/2023] Open
Abstract
Diabetic retinopathy (DR) is a severe complication of diabetes. It affects a large portion of the population of the Kingdom of Saudi Arabia. Existing systems assist clinicians in treating DR patients. However, these systems entail significantly high computational costs. In addition, dataset imbalances may lead existing DR detection systems to produce false positive outcomes. Therefore, the author intended to develop a lightweight deep-learning (DL)-based DR-severity grading system that could be used with limited computational resources. The proposed model followed an image pre-processing approach to overcome the noise and artifacts found in fundus images. A feature extraction process using the You Only Look Once (Yolo) V7 technique was suggested. It was used to provide feature sets. The author employed a tailored quantum marine predator algorithm (QMPA) for selecting appropriate features. A hyperparameter-optimized MobileNet V3 model was utilized for predicting severity levels using images. The author generalized the proposed model using the APTOS and EyePacs datasets. The APTOS dataset contained 5590 fundus images, whereas the EyePacs dataset included 35,100 images. The outcome of the comparative analysis revealed that the proposed model achieved an accuracy of 98.0 and 98.4 and an F1 Score of 93.7 and 93.1 in the APTOS and EyePacs datasets, respectively. In terms of computational complexity, the proposed DR model required fewer parameters, fewer floating-point operations (FLOPs), a lower learning rate, and less training time to learn the key patterns of the fundus images. The lightweight nature of the proposed model can allow healthcare centers to serve patients in remote locations. The proposed model can be implemented as a mobile application to support clinicians in treating DR patients. In the future, the author will focus on improving the proposed model's efficiency to detect DR from low-quality fundus images.
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Affiliation(s)
- Abdul Rahaman Wahab Sait
- Department of Documents and Archive, Center of Documents and Administrative Communication, King Faisal University, P.O. Box 400, Hofuf 31982, Al-Ahsa, Saudi Arabia
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Bashir I, Sajid MZ, Kalsoom R, Ali Khan N, Qureshi I, Abbas F, Abbas Q. RDS-DR: An Improved Deep Learning Model for Classifying Severity Levels of Diabetic Retinopathy. Diagnostics (Basel) 2023; 13:3116. [PMID: 37835859 PMCID: PMC10572213 DOI: 10.3390/diagnostics13193116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/14/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
A well-known eye disorder called diabetic retinopathy (DR) is linked to elevated blood glucose levels. Cotton wool spots, confined veins in the cranial nerve, AV nicking, and hemorrhages in the optic disc are some of its symptoms, which often appear later. Serious side effects of DR might include vision loss, damage to the visual nerves, and obstruction of the retinal arteries. Researchers have devised an automated method utilizing AI and deep learning models to enable the early diagnosis of this illness. This research gathered digital fundus images from renowned Pakistani eye hospitals to generate a new "DR-Insight" dataset and known online sources. A novel methodology named the residual-dense system (RDS-DR) was then devised to assess diabetic retinopathy. To develop this model, we have integrated residual and dense blocks, along with a transition layer, into a deep neural network. The RDS-DR system is trained on the collected dataset of 9860 fundus images. The RDS-DR categorization method demonstrated an impressive accuracy of 97.5% on this dataset. These findings show that the model produces beneficial outcomes and may be used by healthcare practitioners as a diagnostic tool. It is important to emphasize that the system's goal is to augment optometrists' expertise rather than replace it. In terms of accuracy, the RDS-DR technique fared better than the cutting-edge models VGG19, VGG16, Inception V-3, and Xception. This emphasizes how successful the suggested method is for classifying diabetic retinopathy (DR).
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Affiliation(s)
- Ijaz Bashir
- Department of Computer Software Engineering, Military College of Signals, National University of Sciences and Technology, Islamabad 44000, Pakistan; (I.B.); (M.Z.S.); (N.A.K.)
| | - Muhammad Zaheer Sajid
- Department of Computer Software Engineering, Military College of Signals, National University of Sciences and Technology, Islamabad 44000, Pakistan; (I.B.); (M.Z.S.); (N.A.K.)
| | - Rizwana Kalsoom
- Faculty of Computer Science and Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23460, Pakistan;
| | - Nauman Ali Khan
- Department of Computer Software Engineering, Military College of Signals, National University of Sciences and Technology, Islamabad 44000, Pakistan; (I.B.); (M.Z.S.); (N.A.K.)
| | - Imran Qureshi
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia;
| | - Fakhar Abbas
- Centre for Trusted Internet and Community, National University of Singapore (NUS), Singapore 119228, Singapore;
| | - Qaisar Abbas
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia;
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Sajid MZ, Hamid MF, Youssef A, Yasmin J, Perumal G, Qureshi I, Naqi SM, Abbas Q. DR-NASNet: Automated System to Detect and Classify Diabetic Retinopathy Severity Using Improved Pretrained NASNet Model. Diagnostics (Basel) 2023; 13:2645. [PMID: 37627904 PMCID: PMC10453689 DOI: 10.3390/diagnostics13162645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 07/25/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
Diabetes is a widely spread disease that significantly affects people's lives. The leading cause is uncontrolled levels of blood glucose, which develop eye defects over time, including Diabetic Retinopathy (DR), which results in severe visual loss. The primary factor causing blindness is considered to be DR in diabetic patients. DR treatment tries to control the disease's severity, as it is irreversible. The primary goal of this effort is to create a reliable method for automatically detecting the severity of DR. This paper proposes a new automated system (DR-NASNet) to detect and classify DR severity using an improved pretrained NASNet Model. To develop the DR-NASNet system, we first utilized a preprocessing technique that takes advantage of Ben Graham and CLAHE to lessen noise, emphasize lesions, and ultimately improve DR classification performance. Taking into account the imbalance between classes in the dataset, data augmentation procedures were conducted to control overfitting. Next, we have integrated dense blocks into the NASNet architecture to improve the effectiveness of classification results for five severity levels of DR. In practice, the DR-NASNet model achieves state-of-the-art results with a smaller model size and lower complexity. To test the performance of the DR-NASNet system, a combination of various datasets is used in this paper. To learn effective features from DR images, we used a pretrained model on the dataset. The last step is to put the image into one of five categories: No DR, Mild, Moderate, Proliferate, or Severe. To carry this out, the classifier layer of a linear SVM with a linear activation function must be added. The DR-NASNet system was tested using six different experiments. The system achieves 96.05% accuracy with the challenging DR dataset. The results and comparisons demonstrate that the DR-NASNet system improves a model's performance and learning ability. As a result, the DR-NASNet system provides assistance to ophthalmologists by describing an effective system for classifying early-stage levels of DR.
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Affiliation(s)
- Muhammad Zaheer Sajid
- Department of Computer Software Engineering, Military College of Signals (MCS), National University of Science and Technology, Islamabad 44000, Pakistan; (M.Z.S.)
| | - Muhammad Fareed Hamid
- Department of Electrical Engineering, Military College of Signals (MCS), National University of Science and Technology, Islamabad 44000, Pakistan
| | - Ayman Youssef
- Department of Computers and Systems, Electronics Research Institute, Cairo 12622, Egypt;
| | - Javeria Yasmin
- Department of Computer Software Engineering, Military College of Signals (MCS), National University of Science and Technology, Islamabad 44000, Pakistan; (M.Z.S.)
| | - Ganeshkumar Perumal
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (G.P.); (I.Q.)
| | - Imran Qureshi
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (G.P.); (I.Q.)
| | - Syed Muhammad Naqi
- Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan;
| | - Qaisar Abbas
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (G.P.); (I.Q.)
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Zhu D, Ge A, Chen X, Wang Q, Wu J, Liu S. Supervised Contrastive Learning with Angular Margin for the Detection and Grading of Diabetic Retinopathy. Diagnostics (Basel) 2023; 13:2389. [PMID: 37510133 PMCID: PMC10378050 DOI: 10.3390/diagnostics13142389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 07/06/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Many researchers have realized the intelligent medical diagnosis of diabetic retinopathy (DR) from fundus images by using deep learning methods, including supervised contrastive learning (SupCon). However, although SupCon brings label information into the calculation of contrastive learning, it does not distinguish between augmented positives and same-label positives. As a result, we propose the concept of Angular Margin and incorporate it into SupCon to address this issue. To demonstrate the effectiveness of our strategy, we tested it on two datasets for the detection and grading of DR. To align with previous work, Accuracy, Precision, Recall, F1, and AUC were selected as evaluation metrics. Moreover, we also chose alignment and uniformity to verify the effect of representation learning and UMAP (Uniform Manifold Approximation and Projection) to visualize fundus image embeddings. In summary, DR detection achieved state-of-the-art results across all metrics, with Accuracy = 98.91, Precision = 98.93, Recall = 98.90, F1 = 98.91, and AUC = 99.80. The grading also attained state-of-the-art results in terms of Accuracy and AUC, which were 85.61 and 93.97, respectively. The experimental results demonstrate that Angular Margin is an excellent intelligent medical diagnostic algorithm, performing well in both DR detection and grading tasks.
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Affiliation(s)
- Dongsheng Zhu
- Academy for Engineering & Technology, Fudan University, Shanghai 200433, China
| | - Aiming Ge
- Academy for Engineering & Technology, Fudan University, Shanghai 200433, China
- School of Information Science and Technology, Fudan University, Shanghai 200433, China
| | - Xindi Chen
- Academy for Engineering & Technology, Fudan University, Shanghai 200433, China
| | - Qiuyang Wang
- School of Information Science and Technology, Fudan University, Shanghai 200433, China
| | - Jiangbo Wu
- School of Information Science and Technology, Fudan University, Shanghai 200433, China
| | - Shuo Liu
- School of Information Science and Technology, Fudan University, Shanghai 200433, China
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Alwakid G, Gouda W, Humayun M. Enhancement of Diabetic Retinopathy Prognostication Using Deep Learning, CLAHE, and ESRGAN. Diagnostics (Basel) 2023; 13:2375. [PMID: 37510123 PMCID: PMC10378524 DOI: 10.3390/diagnostics13142375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/07/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
One of the primary causes of blindness in the diabetic population is diabetic retinopathy (DR). Many people could have their sight saved if only DR were detected and treated in time. Numerous Deep Learning (DL)-based methods have been presented to improve human analysis. Using a DL model with three scenarios, this research classified DR and its severity stages from fundus images using the "APTOS 2019 Blindness Detection" dataset. Following the adoption of the DL model, augmentation methods were implemented to generate a balanced dataset with consistent input parameters across all test scenarios. As a last step in the categorization process, the DenseNet-121 model was employed. Several methods, including Enhanced Super-resolution Generative Adversarial Networks (ESRGAN), Histogram Equalization (HIST), and Contrast Limited Adaptive HIST (CLAHE), have been used to enhance image quality in a variety of contexts. The suggested model detected the DR across all five APTOS 2019 grading process phases with the highest test accuracy of 98.36%, top-2 accuracy of 100%, and top-3 accuracy of 100%. Further evaluation criteria (precision, recall, and F1-score) for gauging the efficacy of the proposed model were established with the help of APTOS 2019. Furthermore, comparing CLAHE + ESRGAN against both state-of-the-art technology and other recommended methods, it was found that its use was more effective in DR classification.
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Affiliation(s)
- Ghadah Alwakid
- Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakakah 72341, Al Jouf, Saudi Arabia
| | - Walaa Gouda
- Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
| | - Mamoona Humayun
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah 72341, Al Jouf, Saudi Arabia
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8
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Retinal image blood vessel classification using hybrid deep learning in cataract diseased fundus images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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Durai DBJ, Jaya T. Automatic severity grade classification of diabetic retinopathy using deformable ladder Bi attention U-net and deep adaptive CNN. Med Biol Eng Comput 2023:10.1007/s11517-023-02860-9. [PMID: 37338737 DOI: 10.1007/s11517-023-02860-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 05/25/2023] [Indexed: 06/21/2023]
Abstract
Long-term exposure to diabetes mellitus leads to the formation of diabetic retinopathy (DR), which can cause vision loss in working-age adults. Early stage diagnosis of DR is highly essential for preventing vision loss and preserving vision in people with diabetes. The motivation behind the severity grade classification of DR is to develop an automated system that can assist ophthalmologists and healthcare professionals in the diagnosis and management of DR. However, existing methods suffer from variability in image quality, similar structures of the normal and lesion regions, high dimensional features, variability in disease manifestations, small datasets, high training loss, model complexity, and overfitting, which leads to high misclassification errors in the severity grading system. Hence, there is a need to develop an automated system using improved deep learning techniques to provide a reliable and consistent grading of DR severity with high classification accuracy using fundus images. To solve these issues, we proposes a Deformable Ladder Bi attention U-shaped encoder-decoder network and Deep Adaptive Convolutional Neural Network (DLBUnet-DACNN) for accurate severity classification of DR. The DLBUnet performs lesion segmentation that can be divided into three parts: the encoder, the central processing module and the decoder. In the encoder part, deformable convolution is used instead of convolution to learn different shapes of the lesion by understanding the offset location. Afterwards, Ladder Atrous Spatial Pyramidal Pooling (LASPP) using variable dilation rates is introduced in the central processing module. LASPP enhance the tiny lesion features and variable dilation rates avoid gridding effects and can learn better global context information. Then the decoder part uses a bi-attention layer contains spatial and channel attention, which can learn contour and edges of the lesion accurately. Finally, the severity of DR is classified using a DACNN by extracting the discriminative features from the segmentation results. Experiments are conducted on the Messidor-2, Kaggle, and Messidor datasets. Our proposed method DLBUnet-DACNN achieves better results in terms of accuracy of 98.2, recall of 0.987, kappa coefficient of 0.993, precision of 0.98, F1-score of 0.981, Matthews Correlation Coefficient (MCC) of 0.93 and Classification Success Index (CSI) of 0.96 when compared to existing methods.
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Affiliation(s)
- D Binny Jeba Durai
- Department of Electronics and Communication Engineering, Udaya School of Engineering, Vellamodi, India.
| | - T Jaya
- Department of Electronics and Communication Engineering, C.S.I. Institute of Technology, Thovalai, India
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Alwakid G, Gouda W, Humayun M. Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement. Healthcare (Basel) 2023; 11:healthcare11060863. [PMID: 36981520 PMCID: PMC10048517 DOI: 10.3390/healthcare11060863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/11/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023] Open
Abstract
Vision loss can be avoided if diabetic retinopathy (DR) is diagnosed and treated promptly. The main five DR stages are none, moderate, mild, proliferate, and severe. In this study, a deep learning (DL) model is presented that diagnoses all five stages of DR with more accuracy than previous methods. The suggested method presents two scenarios: case 1 with image enhancement using a contrast limited adaptive histogram equalization (CLAHE) filtering algorithm in conjunction with an enhanced super-resolution generative adversarial network (ESRGAN), and case 2 without image enhancement. Augmentation techniques were then performed to generate a balanced dataset utilizing the same parameters for both cases. Using Inception-V3 applied to the Asia Pacific Tele-Ophthalmology Society (APTOS) datasets, the developed model achieved an accuracy of 98.7% for case 1 and 80.87% for case 2, which is greater than existing methods for detecting the five stages of DR. It was demonstrated that using CLAHE and ESRGAN improves a model’s performance and learning ability.
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Affiliation(s)
- Ghadah Alwakid
- Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakakah 72341, Al Jouf, Saudi Arabia;
| | - Walaa Gouda
- Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt;
| | - Mamoona Humayun
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah 72341, Al Jouf, Saudi Arabia
- Correspondence:
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Gul Y, Muezzinoglu T, Kilicarslan G, Dogan S, Tuncer T. Application of the deep transfer learning framework for hydatid cyst classification using CT images. Soft comput 2023. [DOI: 10.1007/s00500-023-07945-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
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12
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A Prospective Study on Diabetic Retinopathy Detection Based on Modify Convolutional Neural Network Using Fundus Images at Sindh Institute of Ophthalmology & Visual Sciences. Diagnostics (Basel) 2023; 13:diagnostics13030393. [PMID: 36766498 PMCID: PMC9914220 DOI: 10.3390/diagnostics13030393] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/11/2023] [Accepted: 01/13/2023] [Indexed: 01/24/2023] Open
Abstract
Diabetic Retinopathy (DR) is the most common complication that arises due to diabetes, and it affects the retina. It is the leading cause of blindness globally, and early detection can protect patients from losing sight. However, the early detection of Diabetic Retinopathy is an difficult task that needs clinical experts' interpretation of fundus images. In this study, a deep learning model was trained and validated on a private dataset and tested in real time at the Sindh Institute of Ophthalmology & Visual Sciences (SIOVS). The intelligent model evaluated the quality of the test images. The implemented model classified the test images into DR-Positive and DR-Negative ones. Furthermore, the results were reviewed by clinical experts to assess the model's performance. A total number of 398 patients, including 232 male and 166 female patients, were screened for five weeks. The model achieves 93.72% accuracy, 97.30% sensitivity, and 92.90% specificity on the test data as labelled by clinical experts on Diabetic Retinopathy.
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Alwakid G, Gouda W, Humayun M, Jhanjhi NZ. Deep learning-enhanced diabetic retinopathy image classification. Digit Health 2023; 9:20552076231194942. [PMID: 37588156 PMCID: PMC10426308 DOI: 10.1177/20552076231194942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/28/2023] [Indexed: 08/18/2023] Open
Abstract
Objective Diabetic retinopathy (DR) can sometimes be treated and prevented from causing irreversible vision loss if caught and treated properly. In this work, a deep learning (DL) model is employed to accurately identify all five stages of DR. Methods The suggested methodology presents two examples, one with and one without picture augmentation. A balanced dataset meeting the same criteria in both cases is then generated using augmentative methods. The DenseNet-121-rendered model on the Asia Pacific Tele-Ophthalmology Society (APTOS) and dataset for diabetic retinopathy (DDR) datasets performed exceptionally well when compared to other methods for identifying the five stages of DR. Results Our propose model achieved the highest test accuracy of 98.36%, top-2 accuracy of 100%, and top-3 accuracy of 100% for the APTOS dataset, and the highest test accuracy of 79.67%, top-2 accuracy of 92.%76, and top-3 accuracy of 98.94% for the DDR dataset. Additional criteria (precision, recall, and F1-score) for gauging the efficacy of the proposed model were established with the help of APTOS and DDR. Conclusions It was discovered that feeding a model with higher-quality photographs increased its efficiency and ability for learning, as opposed to both state-of-the-art technology and the other, non-enhanced model.
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Affiliation(s)
- Ghadah Alwakid
- Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakakah, Saudi Arabia
| | - Walaa Gouda
- Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt
| | - Mamoona Humayun
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah, Saudi Arabia
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Alwakid G, Gouda W, Humayun M, Jhanjhi NZ. Enhancing diabetic retinopathy classification using deep learning. Digit Health 2023; 9:20552076231203676. [PMID: 37766903 PMCID: PMC10521302 DOI: 10.1177/20552076231203676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
Prolonged hyperglycemia can cause diabetic retinopathy (DR), which is a major contributor to blindness. Numerous incidences of DR may be avoided if it were identified and addressed promptly. Throughout recent years, many deep learning (DL)-based algorithms have been proposed to facilitate psychometric testing. Utilizing DL model that encompassed four scenarios, DR and its stages were identified in this study using retinal scans from the "Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 Blindness Detection" dataset. Adopting a DL model then led to the use of augmentation strategies that produced a comprehensive dataset with consistent hyper parameters across all test cases. As a further step in the classification process, we used a Convolutional Neural Network model. Different enhancement methods have been used to raise visual quality. The proposed approach detected the DR with a highest experimental result of 97.83%, a top-2 accuracy of 99.31%, and a top-3 accuracy of 99.88% across all the 5 severity stages of the APTOS 2019 evaluation employing CLAHE and ESRGAN techniques for image enhancement. In addition, we employed APTOS 2019 to develop a set of evaluation metrics (precision, recall, and F1-score) to use in analyzing the efficacy of the suggested model. The proposed approach was also proven to be more efficient at DR location than both state-of-the-art technology and conventional DL.
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Affiliation(s)
- Ghadah Alwakid
- Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakakah, Al Jouf, Saudi Arabia
| | - Walaa Gouda
- Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt
| | - Mamoona Humayun
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah, Al Jouf, Saudi Arabia
| | - NZ Jhanjhi
- School of Computer Science and Engineering (SCE), Taylor's University, Subang Jaya, Malaysia
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EDLDR: An Ensemble Deep Learning Technique for Detection and Classification of Diabetic Retinopathy. Diagnostics (Basel) 2022; 13:diagnostics13010124. [PMID: 36611416 PMCID: PMC9818466 DOI: 10.3390/diagnostics13010124] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 12/24/2022] [Accepted: 12/26/2022] [Indexed: 01/03/2023] Open
Abstract
Diabetic retinopathy (DR) is an ophthalmological disease that causes damage in the blood vessels of the eye. DR causes clotting, lesions or haemorrhage in the light-sensitive region of the retina. Person suffering from DR face loss of vision due to the formation of exudates or lesions in the retina. The detection of DR is critical to the successful treatment of patients suffering from DR. The retinal fundus images may be used for the detection of abnormalities leading to DR. In this paper, an automated ensemble deep learning model is proposed for the detection and classification of DR. The ensembling of a deep learning model enables better predictions and achieves better performance than any single contributing model. Two deep learning models, namely modified DenseNet101 and ResNeXt, are ensembled for the detection of diabetic retinopathy. The ResNeXt model is an improvement over the existing ResNet models. The model includes a shortcut from the previous block to next block, stacking layers and adapting split-transform-merge strategy. The model has a cardinality parameter that specifies the number of transformations. The DenseNet model gives better feature use efficiency as the dense blocks perform concatenation. The ensembling of these two models is performed using normalization over the classes followed by maximum a posteriori over the class outputs to compute the final class label. The experiments are conducted on two datasets APTOS19 and DIARETDB1. The classifications are carried out for both two classes and five classes. The images are pre-processed using CLAHE method for histogram equalization. The dataset has a high-class imbalance and the images of the non-proliferative type are very low, therefore, GAN-based augmentation technique is used for data augmentation. The results obtained from the proposed method are compared with other existing methods. The comparison shows that the proposed method has higher accuracy, precision and recall for both two classes and five classes. The proposed method has an accuracy of 86.08 for five classes and 96.98% for two classes. The precision and recall for two classes are 0.97. For five classes also, the precision and recall are high, i.e., 0.76 and 0.82, respectively.
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Guo X, Li X, Lin Q, Li G, Hu X, Che S. Joint grading of diabetic retinopathy and diabetic macular edema using an adaptive attention block and semisupervised learning. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04295-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Barua PD, Baygin N, Dogan S, Baygin M, Arunkumar N, Fujita H, Tuncer T, Tan RS, Palmer E, Azizan MMB, Kadri NA, Acharya UR. Automated detection of pain levels using deep feature extraction from shutter blinds-based dynamic-sized horizontal patches with facial images. Sci Rep 2022; 12:17297. [PMID: 36241674 PMCID: PMC9568538 DOI: 10.1038/s41598-022-21380-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 09/27/2022] [Indexed: 01/10/2023] Open
Abstract
Pain intensity classification using facial images is a challenging problem in computer vision research. This work proposed a patch and transfer learning-based model to classify various pain intensities using facial images. The input facial images were segmented into dynamic-sized horizontal patches or "shutter blinds". A lightweight deep network DarkNet19 pre-trained on ImageNet1K was used to generate deep features from the shutter blinds and the undivided resized segmented input facial image. The most discriminative features were selected from these deep features using iterative neighborhood component analysis, which were then fed to a standard shallow fine k-nearest neighbor classifier for classification using tenfold cross-validation. The proposed shutter blinds-based model was trained and tested on datasets derived from two public databases-University of Northern British Columbia-McMaster Shoulder Pain Expression Archive Database and Denver Intensity of Spontaneous Facial Action Database-which both comprised four pain intensity classes that had been labeled by human experts using validated facial action coding system methodology. Our shutter blinds-based classification model attained more than 95% overall accuracy rates on both datasets. The excellent performance suggests that the automated pain intensity classification model can be deployed to assist doctors in the non-verbal detection of pain using facial images in various situations (e.g., non-communicative patients or during surgery). This system can facilitate timely detection and management of pain.
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Affiliation(s)
- Prabal Datta Barua
- grid.1048.d0000 0004 0473 0844School of Business (Information System), University of Southern Queensland, Toowoomba, QLD 4350 Australia ,grid.117476.20000 0004 1936 7611Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007 Australia
| | - Nursena Baygin
- grid.16487.3c0000 0000 9216 0511Department of Computer Engineering, College of Engineering, Kafkas University, Kars, Turkey
| | - Sengul Dogan
- grid.411320.50000 0004 0574 1529Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Mehmet Baygin
- grid.449062.d0000 0004 0399 2738Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey
| | - N. Arunkumar
- Rathinam College of Engineering, Coimbatore, India
| | - Hamido Fujita
- Faculty of Information Technology, HUTECH University of Technology, Ho Chi Minh City, Viet Nam ,grid.4489.10000000121678994Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, Granada, Spain ,grid.443998.b0000 0001 2172 3919Regional Research Center, Iwate Prefectural University, Iwate, Japan
| | - Turker Tuncer
- grid.411320.50000 0004 0574 1529Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Ru-San Tan
- grid.419385.20000 0004 0620 9905Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore ,grid.428397.30000 0004 0385 0924Duke-NUS Medical School, Singapore, Singapore
| | - Elizabeth Palmer
- grid.430417.50000 0004 0640 6474Centre of Clinical Genetics, Sydney Children’s Hospitals Network, Randwick, 2031 Australia ,grid.1005.40000 0004 4902 0432School of Women’s and Children’s Health, University of New South Wales, Randwick, 2031 Australia
| | - Muhammad Mokhzaini Bin Azizan
- grid.462995.50000 0001 2218 9236Department of Electrical and Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia (USIM), Nilai, Malaysia
| | - Nahrizul Adib Kadri
- grid.10347.310000 0001 2308 5949Department of Biomedical Engineering, Faculty of Engineering, University Malaya, 50603 Kuala Lumpur, Malaysia
| | - U. Rajendra Acharya
- grid.462630.50000 0000 9158 4937Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489 Singapore ,grid.443365.30000 0004 0388 6484Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore, Singapore ,grid.252470.60000 0000 9263 9645Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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