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Naing SL, Aimmanee P. Automated optic disk segmentation for optic disk edema classification using factorized gradient vector flow. Sci Rep 2024; 14:371. [PMID: 38172282 PMCID: PMC10764308 DOI: 10.1038/s41598-023-50908-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/27/2023] [Indexed: 01/05/2024] Open
Abstract
One significant ocular symptom of neuro-ophthalmic disorders of the optic disk (OD) is optic disk edema (ODE). The etiologies of ODE are broad, with various symptoms and effects. Early detection of ODE can prevent potential vision loss and fatal vision problems. The texture of edematous OD significantly differs from the non-edematous OD in retinal images. As a result, techniques that usually work for non-edematous cases may not work well for edematous cases. We propose a fully automatic OD classification of edematous and non-edematous OD on fundus image collections containing a mixture of edematous and non-edematous ODs. The proposed algorithm involved localization, segmentation, and classification of edematous and non-edematous OD. The factorized gradient vector flow (FGVF) was used to segment the ODs. The OD type was classified using a linear support vector machine (SVM) based on 27 features extracted from the vessels, GLCM, color, and intensity line profile. The proposed method was tested on 295 images with 146 edematous cases and 149 non-edematous cases from three datasets. The segmentation achieves an average precision of 88.41%, recall of 89.35%, and F1-Score of 86.53%. The average classification accuracy is 99.40% and outperforms the state-of-the-art method by 3.43%.
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Affiliation(s)
- Seint Lei Naing
- School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, 131 Moo 5, Tiwanon Rd, Bangkadi, Meung, Patumthani, 12000, Thailand
| | - Pakinee Aimmanee
- School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, 131 Moo 5, Tiwanon Rd, Bangkadi, Meung, Patumthani, 12000, Thailand.
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Guo T, Liu K, Zou H, Xu X, Yang J, Yu Q. Refined image quality assessment for color fundus photography based on deep learning. Digit Health 2024; 10:20552076231207582. [PMID: 38425654 PMCID: PMC10903193 DOI: 10.1177/20552076231207582] [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: 09/26/2023] [Indexed: 03/02/2024] Open
Abstract
Purpose Color fundus photography is widely used in clinical and screening settings for eye diseases. Poor image quality greatly affects the reliability of further evaluation and diagnosis. In this study, we developed an automated assessment module for color fundus photography image quality assessment using deep learning. Methods A total of 55,931 color fundus photography images from multiple centers in Shanghai and the public database were collected and annotated as training, validation, and testing data sets. The pre-diagnosis image quality assessment module based on the multi-task deep neural network was designed. The detailed criterion of color fundus photography image quality including three subcategories with three levels of grading was applied to improve precision and objectivity. The auxiliary tasks such as the localization of the optic nerve head and macula, the classification of laterality, and the field of view were also included to assist the quality assessment. Finally, we validated our module internally and externally by evaluating the area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, and quadratic weighted Kappa. Results The "Location" subcategory achieved area under the receiver operating characteristic curves of 0.991, 0.920, and 0.946 for the three grades, respectively. The "Clarity" subcategory achieved area under the receiver operating characteristic curves of 0.980, 0.917, and 0.954 for the three grades, respectively. The "Artifact" subcategory achieved area under the receiver operating characteristic curves of 0.976, 0.952, and 0.986 for the three grades, respectively. The accuracy and Kappa of overall quality reach 88.15% and 89.70%, respectively, on the internal set. These two indicators on the external set were 86.63% and 88.55%, respectively, which were very close to that of the internal set. Conclusions This work showed that our deep module was able to evaluate the color fundus photography image quality using more detailed three subcategories with three grade criteria. The promising results on both internal and external validation indicated the strength and generalizability of our module.
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Affiliation(s)
- Tianjiao Guo
- Institute of Medical Robotics, Shanghai Jiao Tong University, China
- Department of Automation, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, China
- School of Biomedical Engineering, Shanghai Jiao Tong University, China
| | - Kun Liu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Clinical Research Center for Eye Diseases, China
| | - Haidong Zou
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Clinical Research Center for Eye Diseases, China
| | - Xun Xu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Clinical Research Center for Eye Diseases, China
| | - Jie Yang
- Department of Automation, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, China
| | - Qi Yu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Clinical Research Center for Eye Diseases, China
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Abbas Q, Albathan M, Altameem A, Almakki RS, Hussain A. Deep-Ocular: Improved Transfer Learning Architecture Using Self-Attention and Dense Layers for Recognition of Ocular Diseases. Diagnostics (Basel) 2023; 13:3165. [PMID: 37891986 PMCID: PMC10605427 DOI: 10.3390/diagnostics13203165] [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: 08/28/2023] [Revised: 10/07/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023] Open
Abstract
It is difficult for clinicians or less-experienced ophthalmologists to detect early eye-related diseases. By hand, eye disease diagnosis is labor-intensive, prone to mistakes, and challenging because of the variety of ocular diseases such as glaucoma (GA), diabetic retinopathy (DR), cataract (CT), and normal eye-related diseases (NL). An automated ocular disease detection system with computer-aided diagnosis (CAD) tools is required to recognize eye-related diseases. Nowadays, deep learning (DL) algorithms enhance the classification results of retinograph images. To address these issues, we developed an intelligent detection system based on retinal fundus images. To create this system, we used ODIR and RFMiD datasets, which included various retinographics of distinct classes of the fundus, using cutting-edge image classification algorithms like ensemble-based transfer learning. In this paper, we suggest a three-step hybrid ensemble model that combines a classifier, a feature extractor, and a feature selector. The original image features are first extracted using a pre-trained AlexNet model with an enhanced structure. The improved AlexNet (iAlexNet) architecture with attention and dense layers offers enhanced feature extraction, task adaptability, interpretability, and potential accuracy benefits compared to other transfer learning architectures, making it particularly suited for tasks like retinograph classification. The extracted features are then selected using the ReliefF method, and then the most crucial elements are chosen to minimize the feature dimension. Finally, an XgBoost classifier offers classification outcomes based on the desired features. These classifications represent different ocular illnesses. We utilized data augmentation techniques to control class imbalance issues. The deep-ocular model, based mainly on the AlexNet-ReliefF-XgBoost model, achieves an accuracy of 95.13%. The results indicate the proposed ensemble model can assist dermatologists in making early decisions for the diagnosing and screening of eye-related diseases.
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Affiliation(s)
- Qaisar Abbas
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.A.); (A.A.); (R.S.A.)
| | - Mubarak Albathan
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.A.); (A.A.); (R.S.A.)
| | - Abdullah Altameem
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.A.); (A.A.); (R.S.A.)
| | - Riyad Saleh Almakki
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.A.); (A.A.); (R.S.A.)
| | - Ayyaz Hussain
- Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan;
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Khan MS, Tafshir N, Alam KN, Dhruba AR, Khan MM, Albraikan AA, Almalki FA. Deep Learning for Ocular Disease Recognition: An Inner-Class Balance. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5007111. [PMID: 35528343 PMCID: PMC9071974 DOI: 10.1155/2022/5007111] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/18/2022] [Accepted: 04/12/2022] [Indexed: 12/25/2022]
Abstract
It can be challenging for doctors to identify eye disorders early enough using fundus pictures. Diagnosing ocular illnesses by hand is time-consuming, error-prone, and complicated. Therefore, an automated ocular disease detection system with computer-aided tools is necessary to detect various eye disorders using fundus pictures. Such a system is now possible as a consequence of deep learning algorithms that have improved image classification capabilities. A deep-learning-based approach to targeted ocular detection is presented in this study. For this study, we used state-of-the-art image classification algorithms, such as VGG-19, to classify the ODIR dataset, which contains 5000 images of eight different classes of the fundus. These classes represent different ocular diseases. However, the dataset within these classes is highly unbalanced. To resolve this issue, the work suggested converting this multiclass classification problem into a binary classification problem and taking the same number of images for both classifications. Then, the binary classifications were trained with VGG-19. The accuracy of the VGG-19 model was 98.13% for the normal (N) versus pathological myopia (M) class; the model reached an accuracy of 94.03% for normal (N) versus cataract (C), and the model provided an accuracy of 90.94% for normal (N) versus glaucoma (G). All of the other models also improve the accuracy when the data is balanced.
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Affiliation(s)
- Md Shakib Khan
- Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh
| | - Nafisa Tafshir
- Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh
| | - Kazi Nabiul Alam
- Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh
| | - Abdur Rab Dhruba
- Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh
| | - Mohammad Monirujjaman Khan
- Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh
| | - Amani Abdulrahman Albraikan
- Department of Computer Science, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Faris A. Almalki
- Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
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He J, Li C, Ye J, Qiao Y, Gu L. Self-speculation of clinical features based on knowledge distillation for accurate ocular disease classification. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102491] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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He J, Li C, Ye J, Qiao Y, Gu L. Multi-label ocular disease classification with a dense correlation deep neural network. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102167] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Efficient and robust optic disc detection and fovea localization using region proposal network and cascaded network. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101939] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Zhang J, Ma C, Liu J, Shi G. Penetrating the influence of regularizations on neural network based on information bottleneck theory. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.02.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Abdullah AS, Rahebi J, Özok YE, Aljanabi M. A new and effective method for human retina optic disc segmentation with fuzzy clustering method based on active contour model. Med Biol Eng Comput 2019; 58:25-37. [DOI: 10.1007/s11517-019-02032-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Accepted: 08/13/2019] [Indexed: 10/26/2022]
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Rasta SH, Mohammadi F, Esmaeili M, Javadzadeh A, Tabar HA. The computer based method to diabetic retinopathy assessment in retinal images: a review. ELECTRONIC JOURNAL OF GENERAL MEDICINE 2019. [DOI: 10.29333/ejgm/108619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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