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Huang J, Zhang X, Jin R, Xu T, Jin Z, Shen M, Lv F, Chen J, Liu J. Wavelet-based selection-and-recalibration network for Parkinson's disease screening in OCT images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 256:108368. [PMID: 39154408 DOI: 10.1016/j.cmpb.2024.108368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 07/30/2024] [Accepted: 08/07/2024] [Indexed: 08/20/2024]
Abstract
BACKGROUND AND OBJECTIVE Parkinson's disease (PD) is one of the most prevalent neurodegenerative brain diseases worldwide. Therefore, accurate PD screening is crucial for early clinical intervention and treatment. Recent clinical research indicates that changes in pathology, such as the texture and thickness of the retinal layers, can serve as biomarkers for clinical PD diagnosis based on optical coherence tomography (OCT) images. However, the pathological manifestations of PD in the retinal layers are subtle compared to the more salient lesions associated with retinal diseases. METHODS Inspired by textural edge feature extraction in frequency domain learning, we aim to explore a potential approach to enhance the distinction between the feature distributions in retinal layers of PD cases and healthy controls. In this paper, we introduce a simple yet novel wavelet-based selection and recalibration module to effectively enhance the feature representations of the deep neural network by aggregating the unique clinical properties, such as the retinal layers in each frequency band. We combine this module with the residual block to form a deep network named Wavelet-based Selection and Recalibration Network (WaveSRNet) for automatic PD screening. RESULTS The extensive experiments on a clinical PD-OCT dataset and two publicly available datasets demonstrate that our approach outperforms state-of-the-art methods. Visualization analysis and ablation studies are conducted to enhance the explainability of WaveSRNet in the decision-making process. CONCLUSIONS Our results suggest the potential role of the retina as an assessment tool for PD. Visual analysis shows that PD-related elements include not only certain retinal layers but also the location of the fovea in OCT images.
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Affiliation(s)
- Jingqi Huang
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Xiaoqing Zhang
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China; Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics, Shenzhen institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Richu Jin
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Tao Xu
- The State Key Laboratory of Ophthalmology, Optometry and Vision Science, Wenzhou Medical University, Wenzhou, Zhejiang, China; The Oujiang Laboratory; The Affiliated Eye Hospital, Wenzhou Medical University, 270 Xueyuan Road, Wenzhou, Zhejiang, China
| | - Zi Jin
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Meixiao Shen
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Fan Lv
- The Oujiang Laboratory; The Affiliated Eye Hospital, Wenzhou Medical University, 270 Xueyuan Road, Wenzhou, Zhejiang, China; National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Jiangfan Chen
- The State Key Laboratory of Ophthalmology, Optometry and Vision Science, Wenzhou Medical University, Wenzhou, Zhejiang, China; The Oujiang Laboratory; The Affiliated Eye Hospital, Wenzhou Medical University, 270 Xueyuan Road, Wenzhou, Zhejiang, China
| | - Jiang Liu
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China; The State Key Laboratory of Ophthalmology, Optometry and Vision Science, Wenzhou Medical University, Wenzhou, Zhejiang, China; Singapore Eye Research Institute, 169856, Singapore.
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Kang C, Lo JE, Zhang H, Ng SM, Lin JC, Scott IU, Kalpathy-Cramer J, Liu SHA, Greenberg PB. Artificial intelligence for diagnosing exudative age-related macular degeneration. Cochrane Database Syst Rev 2024; 10:CD015522. [PMID: 39417312 PMCID: PMC11483348 DOI: 10.1002/14651858.cd015522.pub2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
BACKGROUND Age-related macular degeneration (AMD) is a retinal disorder characterized by central retinal (macular) damage. Approximately 10% to 20% of non-exudative AMD cases progress to the exudative form, which may result in rapid deterioration of central vision. Individuals with exudative AMD (eAMD) need prompt consultation with retinal specialists to minimize the risk and extent of vision loss. Traditional methods of diagnosing ophthalmic disease rely on clinical evaluation and multiple imaging techniques, which can be resource-consuming. Tests leveraging artificial intelligence (AI) hold the promise of automatically identifying and categorizing pathological features, enabling the timely diagnosis and treatment of eAMD. OBJECTIVES To determine the diagnostic accuracy of artificial intelligence (AI) as a triaging tool for exudative age-related macular degeneration (eAMD). SEARCH METHODS We searched CENTRAL, MEDLINE, Embase, three clinical trials registries, and Data Archiving and Networked Services (DANS) for gray literature. We did not restrict searches by language or publication date. The date of the last search was April 2024. SELECTION CRITERIA Included studies compared the test performance of algorithms with that of human readers to detect eAMD on retinal images collected from people with AMD who were evaluated at eye clinics in community or academic medical centers, and who were not receiving treatment for eAMD when the images were taken. We included algorithms that were either internally or externally validated or both. DATA COLLECTION AND ANALYSIS Pairs of review authors independently extracted data and assessed study quality using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool with revised signaling questions. For studies that reported more than one set of performance results, we extracted only one set of diagnostic accuracy data per study based on the last development stage or the optimal algorithm as indicated by the study authors. For two-class algorithms, we collected data from the 2x2 table whenever feasible. For multi-class algorithms, we first consolidated data from all classes other than eAMD before constructing the corresponding 2x2 tables. Assuming a common positivity threshold applied by the included studies, we chose random-effects, bivariate logistic models to estimate summary sensitivity and specificity as the primary performance metrics. MAIN RESULTS We identified 36 eligible studies that reported 40 sets of algorithm performance data, encompassing over 16,000 participants and 62,000 images. We included 28 studies (78%) that reported 31 algorithms with performance data in the meta-analysis. The remaining nine studies (25%) reported eight algorithms that lacked usable performance data; we reported them in the qualitative synthesis. Study characteristics and risk of bias Most studies were conducted in Asia, followed by Europe, the USA, and collaborative efforts spanning multiple countries. Most studies identified study participants from the hospital setting, while others used retinal images from public repositories; a few studies did not specify image sources. Based on four of the 36 studies reporting demographic information, the age of the study participants ranged from 62 to 82 years. The included algorithms used various retinal image types as model input, such as optical coherence tomography (OCT) images (N = 15), fundus images (N = 6), and multi-modal imaging (N = 7). The predominant core method used was deep neural networks. All studies that reported externally validated algorithms were at high risk of bias mainly due to potential selection bias from either a two-gate design or the inappropriate exclusion of potentially eligible retinal images (or participants). Findings Only three of the 40 included algorithms were externally validated (7.5%, 3/40). The summary sensitivity and specificity were 0.94 (95% confidence interval (CI) 0.90 to 0.97) and 0.99 (95% CI 0.76 to 1.00), respectively, when compared to human graders (3 studies; 27,872 images; low-certainty evidence). The prevalence of images with eAMD ranged from 0.3% to 49%. Twenty-eight algorithms were reportedly either internally validated (20%, 8/40) or tested on a development set (50%, 20/40); the pooled sensitivity and specificity were 0.93 (95% CI 0.89 to 0.96) and 0.96 (95% CI 0.94 to 0.98), respectively, when compared to human graders (28 studies; 33,409 images; low-certainty evidence). We did not identify significant sources of heterogeneity among these 28 algorithms. Although algorithms using OCT images appeared more homogeneous and had the highest summary specificity (0.97, 95% CI 0.93 to 0.98), they were not superior to algorithms using fundus images alone (0.94, 95% CI 0.89 to 0.97) or multimodal imaging (0.96, 95% CI 0.88 to 0.99; P for meta-regression = 0.239). The median prevalence of images with eAMD was 30% (interquartile range [IQR] 22% to 39%). We did not include eight studies that described nine algorithms (one study reported two sets of algorithm results) to distinguish eAMD from normal images, images of other AMD, or other non-AMD retinal lesions in the meta-analysis. Five of these algorithms were generally based on smaller datasets (range 21 to 218 participants per study) yet with a higher prevalence of eAMD images (range 33% to 66%). Relative to human graders, the reported sensitivity in these studies ranged from 0.95 and 0.97, while the specificity ranged from 0.94 to 0.99. Similarly, using small datasets (range 46 to 106), an additional four algorithms for detecting eAMD from other retinal lesions showed high sensitivity (range 0.96 to 1.00) and specificity (range 0.77 to 1.00). AUTHORS' CONCLUSIONS Low- to very low-certainty evidence suggests that an algorithm-based test may correctly identify most individuals with eAMD without increasing unnecessary referrals (false positives) in either the primary or the specialty care settings. There were significant concerns for applying the review findings due to variations in the eAMD prevalence in the included studies. In addition, among the included algorithm-based tests, diagnostic accuracy estimates were at risk of bias due to study participants not reflecting real-world characteristics, inadequate model validation, and the likelihood of selective results reporting. Limited quality and quantity of externally validated algorithms highlighted the need for high-certainty evidence. This evidence will require a standardized definition for eAMD on different imaging modalities and external validation of the algorithm to assess generalizability.
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Affiliation(s)
- Chaerim Kang
- Division of Ophthalmology, Brown University, Providence, RI, USA
| | - Jui-En Lo
- Department of Internal Medicine, MetroHealth Medical Center/Case Western Reserve University, Cleveland, USA
| | - Helen Zhang
- Program in Liberal Medical Education, Brown University, Providence, RI, USA
| | - Sueko M Ng
- Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - John C Lin
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ingrid U Scott
- Department of Ophthalmology and Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | | | - Su-Hsun Alison Liu
- Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Department of Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Paul B Greenberg
- Division of Ophthalmology, Brown University, Providence, RI, USA
- Section of Ophthalmology, VA Providence Healthcare System, Providence, RI, USA
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Akpinar MH, Sengur A, Faust O, Tong L, Molinari F, Acharya UR. Artificial intelligence in retinal screening using OCT images: A review of the last decade (2013-2023). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108253. [PMID: 38861878 DOI: 10.1016/j.cmpb.2024.108253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/22/2024] [Accepted: 05/25/2024] [Indexed: 06/13/2024]
Abstract
BACKGROUND AND OBJECTIVES Optical coherence tomography (OCT) has ushered in a transformative era in the domain of ophthalmology, offering non-invasive imaging with high resolution for ocular disease detection. OCT, which is frequently used in diagnosing fundamental ocular pathologies, such as glaucoma and age-related macular degeneration (AMD), plays an important role in the widespread adoption of this technology. Apart from glaucoma and AMD, we will also investigate pertinent pathologies, such as epiretinal membrane (ERM), macular hole (MH), macular dystrophy (MD), vitreomacular traction (VMT), diabetic maculopathy (DMP), cystoid macular edema (CME), central serous chorioretinopathy (CSC), diabetic macular edema (DME), diabetic retinopathy (DR), drusen, glaucomatous optic neuropathy (GON), neovascular AMD (nAMD), myopia macular degeneration (MMD) and choroidal neovascularization (CNV) diseases. This comprehensive review examines the role that OCT-derived images play in detecting, characterizing, and monitoring eye diseases. METHOD The 2020 PRISMA guideline was used to structure a systematic review of research on various eye conditions using machine learning (ML) or deep learning (DL) techniques. A thorough search across IEEE, PubMed, Web of Science, and Scopus databases yielded 1787 publications, of which 1136 remained after removing duplicates. Subsequent exclusion of conference papers, review papers, and non-open-access articles reduced the selection to 511 articles. Further scrutiny led to the exclusion of 435 more articles due to lower-quality indexing or irrelevance, resulting in 76 journal articles for the review. RESULTS During our investigation, we found that a major challenge for ML-based decision support is the abundance of features and the determination of their significance. In contrast, DL-based decision support is characterized by a plug-and-play nature rather than relying on a trial-and-error approach. Furthermore, we observed that pre-trained networks are practical and especially useful when working on complex images such as OCT. Consequently, pre-trained deep networks were frequently utilized for classification tasks. Currently, medical decision support aims to reduce the workload of ophthalmologists and retina specialists during routine tasks. In the future, it might be possible to create continuous learning systems that can predict ocular pathologies by identifying subtle changes in OCT images.
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Affiliation(s)
- Muhammed Halil Akpinar
- Department of Electronics and Automation, Vocational School of Technical Sciences, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Abdulkadir Sengur
- Electrical-Electronics Engineering Department, Technology Faculty, Firat University, Elazig, Turkey.
| | - Oliver Faust
- School of Computing and Information Science, Anglia Ruskin University Cambridge Campus, United Kingdom
| | - Louis Tong
- Singapore Eye Research Institute, Singapore, Singapore
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia
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Pang S, Zou B, Xiao X, Peng Q, Yan J, Zhang W, Yue K. A novel approach for automatic classification of macular degeneration OCT images. Sci Rep 2024; 14:19285. [PMID: 39164445 PMCID: PMC11335908 DOI: 10.1038/s41598-024-70175-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 08/13/2024] [Indexed: 08/22/2024] Open
Abstract
Age-related macular degeneration (AMD) and diabetic macular edema (DME) are significant causes of blindness worldwide. The prevalence of these diseases is steadily increasing due to population aging. Therefore, early diagnosis and prevention are crucial for effective treatment. Classification of Macular Degeneration OCT Images is a widely used method for assessing retinal lesions. However, there are two main challenges in OCT image classification: incomplete image feature extraction and lack of prominence in important positional features. To address these challenges, we proposed a deep learning neural network model called MSA-Net, which incorporates our proposed multi-scale architecture and spatial attention mechanism. Our multi-scale architecture is based on depthwise separable convolution, which ensures comprehensive feature extraction from multiple scales while minimizing the growth of model parameters. The spatial attention mechanism is aim to highlight the important positional features in the images, which emphasizes the representation of macular region features in OCT images. We test MSA-NET on the NEH dataset and the UCSD dataset, performing three-class (CNV, DURSEN, and NORMAL) and four-class (CNV, DURSEN, DME, and NORMAL) classification tasks. On the NEH dataset, the accuracy, sensitivity, and specificity are 98.1%, 97.9%, and 98.0%, respectively. After fine-tuning on the UCSD dataset, the accuracy, sensitivity, and specificity are 96.7%, 96.7%, and 98.9%, respectively. Experimental results demonstrate the excellent classification performance and generalization ability of our model compared to previous models and recent well-known OCT classification models, establishing it as a highly competitive intelligence classification approach in the field of macular degeneration.
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Affiliation(s)
- Shilong Pang
- School of Informatics, Hunan University of Chinese Medicine, Changsha, 410208, Hunan, China
| | - Beiji Zou
- School of Informatics, Hunan University of Chinese Medicine, Changsha, 410208, Hunan, China
- School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China
| | - Xiaoxia Xiao
- School of Informatics, Hunan University of Chinese Medicine, Changsha, 410208, Hunan, China.
| | - Qinghua Peng
- School of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410208, Hunan, China
| | - Junfeng Yan
- School of Informatics, Hunan University of Chinese Medicine, Changsha, 410208, Hunan, China
| | - Wensheng Zhang
- School of Informatics, Hunan University of Chinese Medicine, Changsha, 410208, Hunan, China
- University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China
- Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Kejuan Yue
- School of Computer Science, Hunan First Normal University, Changsha, 410205, Hunan, China
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Azizi MM, Abhari S, Sajedi H. Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images. PLoS One 2024; 19:e0304943. [PMID: 38837967 DOI: 10.1371/journal.pone.0304943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 05/21/2024] [Indexed: 06/07/2024] Open
Abstract
Age-related macular degeneration (AMD) is an eye disease that leads to the deterioration of the central vision area of the eye and can gradually result in vision loss in elderly individuals. Early identification of this disease can significantly impact patient treatment outcomes. Furthermore, given the increasing elderly population globally, the importance of automated methods for rapidly monitoring at-risk individuals and accurately diagnosing AMD is growing daily. One standard method for diagnosing AMD is using optical coherence tomography (OCT) images as a non-invasive imaging technology. In recent years, numerous deep neural networks have been proposed for the classification of OCT images. Utilizing pre-trained neural networks can speed up model deployment in related tasks without compromising accuracy. However, most previous methods overlook the feasibility of leveraging pre-existing trained networks to search for an optimal architecture for AMD staging on a new target dataset. In this study, our objective was to achieve an optimal architecture in the efficiency-accuracy trade-off for classifying retinal OCT images. To this end, we employed pre-trained medical vision transformer (MedViT) models. MedViT combines convolutional and transformer neural networks, explicitly designed for medical image classification. Our approach involved pre-training two distinct MedViT models on a source dataset with labels identical to those in the target dataset. This pre-training was conducted in a supervised manner. Subsequently, we evaluated the performance of the pre-trained MedViT models for classifying retinal OCT images from the target Noor Eye Hospital (NEH) dataset into the normal, drusen, and choroidal neovascularization (CNV) classes in zero-shot settings and through five-fold cross-validation. Then, we proposed a stitching approach to search for an optimal model from two MedViT family models. The proposed stitching method is an efficient architecture search algorithm known as stitchable neural networks. Stitchable neural networks create a candidate model in search space for each pair of stitchable layers by inserting a linear layer between them. A pair of stitchable layers consists of layers, each selected from one input model. While stitchable neural networks had previously been tested on more extensive and general datasets, this study demonstrated that stitching networks could also be helpful in smaller medical datasets. The results of this approach indicate that when pre-trained models were available for OCT images from another dataset, it was possible to achieve a model in 100 epochs with an accuracy of over 94.9% in classifying images from the NEH dataset. The results of this study demonstrate the efficacy of stitchable neural networks as a fine-tuning method for OCT image classification. This approach not only leads to higher accuracy but also considers architecture optimization at a reasonable computational cost.
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Affiliation(s)
- Mohammad Mahdi Azizi
- Department of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Setareh Abhari
- Department of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Hedieh Sajedi
- Department of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
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Alizadeh Eghtedar R, Vard A, Malekahmadi M, Peyman A. A new computer-aided diagnosis tool based on deep learning methods for automatic detection of retinal disorders from OCT images. Int Ophthalmol 2024; 44:110. [PMID: 38396074 DOI: 10.1007/s10792-024-03033-9] [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: 03/10/2023] [Accepted: 01/11/2024] [Indexed: 02/25/2024]
Abstract
PURPOSE Early detection of retinal disorders using optical coherence tomography (OCT) images can prevent vision loss. Since manual screening can be time-consuming, tedious, and fallible, we present a reliable computer-aided diagnosis (CAD) software based on deep learning. Also, we made efforts to increase the interpretability of the deep learning methods, overcome their vague and black box nature, and also understand their behavior in the diagnosis. METHODS We propose a novel method to improve the interpretability of the used deep neural network by embedding the rich semantic information of abnormal areas based on the ophthalmologists' interpretations and medical descriptions in the OCT images. Finally, we trained the classification network on a small subset of the online publicly available University of California San Diego (UCSD) dataset with an overall of 29,800 OCT images. RESULTS The experimental results on the 1000 test OCT images show that the proposed method achieves the overall precision, accuracy, sensitivity, and f1-score of 97.6%, 97.6%, 97.6%, and 97.59%, respectively. Also, the heat map images provide a clear region of interest which indicates that the interpretability of the proposed method is increased dramatically. CONCLUSION The proposed software can help ophthalmologists in providing a second opinion to make a decision, and primitive automated diagnoses of retinal diseases and even it can be used as a screening tool, in eye clinics. Also, the improvement of the interpretability of the proposed method causes to increase in the model generalization, and therefore, it will work properly on a wide range of other OCT datasets.
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Affiliation(s)
- Reza Alizadeh Eghtedar
- Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
- Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Alireza Vard
- Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
- Medical Image & Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Mohammad Malekahmadi
- Department of Ophthalmology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
- Isfahan Eye Research Center, Department of Ophthalmology, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Alireza Peyman
- Department of Ophthalmology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
- Isfahan Eye Research Center, Department of Ophthalmology, Isfahan University of Medical Sciences, Isfahan, Iran
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Peng J, Lu J, Zhuo J, Li P. Multi-Scale-Denoising Residual Convolutional Network for Retinal Disease Classification Using OCT. SENSORS (BASEL, SWITZERLAND) 2023; 24:150. [PMID: 38203011 PMCID: PMC10781341 DOI: 10.3390/s24010150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/13/2023] [Accepted: 12/15/2023] [Indexed: 01/12/2024]
Abstract
Macular pathologies can cause significant vision loss. Optical coherence tomography (OCT) images of the retina can assist ophthalmologists in diagnosing macular diseases. Traditional deep learning networks for retinal disease classification cannot extract discriminative features under strong noise conditions in OCT images. To address this issue, we propose a multi-scale-denoising residual convolutional network (MS-DRCN) for classifying retinal diseases. Specifically, the MS-DRCN includes a soft-denoising block (SDB), a multi-scale context block (MCB), and a feature fusion block (FFB). The SDB can determine the threshold for soft thresholding automatically, which removes speckle noise features efficiently. The MCB is designed to capture multi-scale context information and strengthen extracted features. The FFB is dedicated to integrating high-resolution and low-resolution features to precisely identify variable lesion areas. Our approach achieved classification accuracies of 96.4% and 96.5% on the OCT2017 and OCT-C4 public datasets, respectively, outperforming other classification methods. To evaluate the robustness of our method, we introduced Gaussian noise and speckle noise with varying PSNRs into the test set of the OCT2017 dataset. The results of our anti-noise experiments demonstrate that our approach exhibits superior robustness compared with other methods, yielding accuracy improvements ranging from 0.6% to 2.9% when compared with ResNet under various PSNR noise conditions.
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Affiliation(s)
- Jinbo Peng
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Haiko 570228, China; (J.P.); (J.L.)
- Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Haiko 570228, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Science, HUST-Suzhou Institute for Brainsmatics, Jiangsu Industrial Technology Research Institute (JITRI), Suzhou 215100, China
| | - Jinling Lu
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Haiko 570228, China; (J.P.); (J.L.)
- Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Haiko 570228, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Science, HUST-Suzhou Institute for Brainsmatics, Jiangsu Industrial Technology Research Institute (JITRI), Suzhou 215100, China
- Britton Chance Center for Biomedical Photonics and MoE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Junjie Zhuo
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Haiko 570228, China; (J.P.); (J.L.)
- Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Haiko 570228, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Science, HUST-Suzhou Institute for Brainsmatics, Jiangsu Industrial Technology Research Institute (JITRI), Suzhou 215100, China
| | - Pengcheng Li
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Haiko 570228, China; (J.P.); (J.L.)
- Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Haiko 570228, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Science, HUST-Suzhou Institute for Brainsmatics, Jiangsu Industrial Technology Research Institute (JITRI), Suzhou 215100, China
- Britton Chance Center for Biomedical Photonics and MoE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
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Arian R, Vard A, Kafieh R, Plonka G, Rabbani H. A new convolutional neural network based on combination of circlets and wavelets for macular OCT classification. Sci Rep 2023; 13:22582. [PMID: 38114582 PMCID: PMC10730902 DOI: 10.1038/s41598-023-50164-7] [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: 06/17/2023] [Accepted: 12/15/2023] [Indexed: 12/21/2023] Open
Abstract
Artificial intelligence (AI) algorithms, encompassing machine learning and deep learning, can assist ophthalmologists in early detection of various ocular abnormalities through the analysis of retinal optical coherence tomography (OCT) images. Despite considerable progress in these algorithms, several limitations persist in medical imaging fields, where a lack of data is a common issue. Accordingly, specific image processing techniques, such as time-frequency transforms, can be employed in conjunction with AI algorithms to enhance diagnostic accuracy. This research investigates the influence of non-data-adaptive time-frequency transforms, specifically X-lets, on the classification of OCT B-scans. For this purpose, each B-scan was transformed using every considered X-let individually, and all the sub-bands were utilized as the input for a designed 2D Convolutional Neural Network (CNN) to extract optimal features, which were subsequently fed to the classifiers. Evaluating per-class accuracy shows that the use of the 2D Discrete Wavelet Transform (2D-DWT) yields superior outcomes for normal cases, whereas the circlet transform outperforms other X-lets for abnormal cases characterized by circles in their retinal structure (due to the accumulation of fluid). As a result, we propose a novel transform named CircWave by concatenating all sub-bands from the 2D-DWT and the circlet transform. The objective is to enhance the per-class accuracy of both normal and abnormal cases simultaneously. Our findings show that classification results based on the CircWave transform outperform those derived from original images or any individual transform. Furthermore, Grad-CAM class activation visualization for B-scans reconstructed from CircWave sub-bands highlights a greater emphasis on circular formations in abnormal cases and straight lines in normal cases, in contrast to the focus on irrelevant regions in original B-scans. To assess the generalizability of our method, we applied it to another dataset obtained from a different imaging system. We achieved promising accuracies of 94.5% and 90% for the first and second datasets, respectively, which are comparable with results from previous studies. The proposed CNN based on CircWave sub-bands (i.e. CircWaveNet) not only produces superior outcomes but also offers more interpretable results with a heightened focus on features crucial for ophthalmologists.
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Affiliation(s)
- Roya Arian
- Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, 81746-73461, Iran
- Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, 81746-73461, Iran
| | - Alireza Vard
- Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, 81746-73461, Iran
| | - Rahele Kafieh
- Department of Engineering, Durham University, South Road, Durham, UK
| | - Gerlind Plonka
- Institute for Numerical and Applied Mathematics, University of Göttingen, Lotzestr. 16-18, 37083, Göttingen, Germany
| | - Hossein Rabbani
- Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, 81746-73461, Iran.
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9
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Bui PN, Le DT, Bum J, Kim S, Song SJ, Choo H. Multi-Scale Learning with Sparse Residual Network for Explainable Multi-Disease Diagnosis in OCT Images. Bioengineering (Basel) 2023; 10:1249. [PMID: 38002373 PMCID: PMC10669434 DOI: 10.3390/bioengineering10111249] [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: 10/02/2023] [Revised: 10/19/2023] [Accepted: 10/23/2023] [Indexed: 11/26/2023] Open
Abstract
In recent decades, medical imaging techniques have revolutionized the field of disease diagnosis, enabling healthcare professionals to noninvasively observe the internal structures of the human body. Among these techniques, optical coherence tomography (OCT) has emerged as a powerful and versatile tool that allows high-resolution, non-invasive, and real-time imaging of biological tissues. Deep learning algorithms have been successfully employed to detect and classify various retinal diseases in OCT images, enabling early diagnosis and treatment planning. However, existing deep learning algorithms are primarily designed for single-disease diagnosis, which limits their practical application in clinical settings where OCT images often contain symptoms of multiple diseases. In this paper, we propose an effective approach for multi-disease diagnosis in OCT images using a multi-scale learning (MSL) method and a sparse residual network (SRN). Specifically, the MSL method extracts and fuses useful features from images of different sizes to enhance the discriminative capability of a classifier and make the disease predictions interpretable. The SRN is a minimal residual network, where convolutional layers with large kernel sizes are replaced with multiple convolutional layers that have smaller kernel sizes, thereby reducing model complexity while achieving a performance similar to that of existing convolutional neural networks. The proposed multi-scale sparse residual network significantly outperforms existing methods, exhibiting 97.40% accuracy, 95.38% sensitivity, and 98.25% specificity. Experimental results show the potential of our method to improve explainable diagnosis systems for various eye diseases via visual discrimination.
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Affiliation(s)
- Phuoc-Nguyen Bui
- Department of AI Systems Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea;
| | - Duc-Tai Le
- College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, Republic of Korea;
| | - Junghyun Bum
- Sungkyun AI Research Institute, Sungkyunkwan University, Suwon 16419, Republic of Korea;
| | - Seongho Kim
- Department of Ophthalmology, Kangbuk Samsung Hospital, School of Medicine, Sungkyunkwan University, Seoul 03181, Republic of Korea;
| | - Su Jeong Song
- Department of Ophthalmology, Kangbuk Samsung Hospital, School of Medicine, Sungkyunkwan University, Seoul 03181, Republic of Korea;
- Biomedical Institute for Convergence, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Hyunseung Choo
- Department of AI Systems Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea;
- College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, Republic of Korea;
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
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10
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Zhang H, Heinke A, Galang CMB, Deussen DN, Wen B, Bartsch DUG, Freeman WR, Nguyen TQ, An C. Robust AMD Stage Grading with Exclusively OCTA Modality Leveraging 3D Volume. ... IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS. IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION 2023; 2023:2403-2412. [PMID: 39176054 PMCID: PMC11340655 DOI: 10.1109/iccvw60793.2023.00255] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2024]
Abstract
Age-related Macular Degeneration (AMD) is a degenerative eye disease that causes central vision loss. Optical Coherence Tomography Angiography (OCTA) is an emerging imaging modality that aids in the diagnosis of AMD by displaying the pathogenic vessels in the subretinal space. In this paper, we investigate the effectiveness of OCTA from the view of deep classifiers. To the best of our knowledge, this is the first study that solely uses OCTA for AMD stage grading. By developing a 2D classifier based on OCTA projections, we identify that segmentation errors in retinal layers significantly affect the accuracy of classification. To address this issue, we propose analyzing 3D OCTA volumes directly using a 2D convolutional neural network trained with additional projection supervision. Our experimental results show that we achieve over 80% accuracy on a four-stage grading task on both error-free and error-prone test sets, which is significantly higher than 60%, the accuracy of human experts. This demonstrates that OCTA provides sufficient information for AMD stage grading and the proposed 3D volume analyzer is more robust when dealing with OCTA data with segmentation errors.
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Affiliation(s)
- Haochen Zhang
- Electrical and Computer Engineering Department, UC San Diego
| | - Anna Heinke
- Jacobs Retina Center, Shiley Eye Institute, UC San Diego
| | | | | | - Bo Wen
- Electrical and Computer Engineering Department, UC San Diego
| | | | | | - Truong Q Nguyen
- Electrical and Computer Engineering Department, UC San Diego
| | - Cheolhong An
- Electrical and Computer Engineering Department, UC San Diego
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11
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Akinniyi O, Rahman MM, Sandhu HS, El-Baz A, Khalifa F. Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture. Bioengineering (Basel) 2023; 10:823. [PMID: 37508850 PMCID: PMC10376573 DOI: 10.3390/bioengineering10070823] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/01/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Accurate noninvasive diagnosis of retinal disorders is required for appropriate treatment or precision medicine. This work proposes a multi-stage classification network built on a multi-scale (pyramidal) feature ensemble architecture for retinal image classification using optical coherence tomography (OCT) images. First, a scale-adaptive neural network is developed to produce multi-scale inputs for feature extraction and ensemble learning. The larger input sizes yield more global information, while the smaller input sizes focus on local details. Then, a feature-rich pyramidal architecture is designed to extract multi-scale features as inputs using DenseNet as the backbone. The advantage of the hierarchical structure is that it allows the system to extract multi-scale, information-rich features for the accurate classification of retinal disorders. Evaluation on two public OCT datasets containing normal and abnormal retinas (e.g., diabetic macular edema (DME), choroidal neovascularization (CNV), age-related macular degeneration (AMD), and Drusen) and comparison against recent networks demonstrates the advantages of the proposed architecture's ability to produce feature-rich classification with average accuracy of 97.78%, 96.83%, and 94.26% for the first (binary) stage, second (three-class) stage, and all-at-once (four-class) classification, respectively, using cross-validation experiments using the first dataset. In the second dataset, our system showed an overall accuracy, sensitivity, and specificity of 99.69%, 99.71%, and 99.87%, respectively. Overall, the tangible advantages of the proposed network for enhanced feature learning might be used in various medical image classification tasks where scale-invariant features are crucial for precise diagnosis.
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Affiliation(s)
- Oluwatunmise Akinniyi
- Department of Computer Science, School of Computer, Mathematical and Natural Sciences, Morgan State University, Baltimore, MD 21251, USA
| | - Md Mahmudur Rahman
- Department of Computer Science, School of Computer, Mathematical and Natural Sciences, Morgan State University, Baltimore, MD 21251, USA
| | - Harpal Singh Sandhu
- Bioengineering Department, University of Louisville, Louisville, KY 20292, USA
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 20292, USA
| | - Fahmi Khalifa
- Electronics and Communications Engineering Department, Mansoura University, Mansoura 35516, Egypt
- Electrical and Computer Engineering Department, Morgan State University, Baltimore MD 21251, USA
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12
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Sahoo M, Mitra M, Pal S. Improved Detection of Dry Age-Related Macular Degeneration from Optical Coherence Tomography Images using Adaptive Window Based Feature Extraction and Weighted Ensemble Based Classification Approach. Photodiagnosis Photodyn Ther 2023:103629. [PMID: 37244451 DOI: 10.1016/j.pdpdt.2023.103629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 05/20/2023] [Accepted: 05/22/2023] [Indexed: 05/29/2023]
Abstract
BACKGROUND Dry Age-related macular degeneration (AMD), which affects the older population, can lead to blindness when left untreated. Preventing vision loss in elderly needs early identification. Dry-AMD diagnosis is still time-consuming and very subjective, depending on the ophthalmologist. Setting up a thorough eye-screening system to find Dry-AMD is a very difficult task. METHODOLOGY This study aims to develop a weighted majority voting (WMV) ensemble-based prediction model to diagnose Dry-AMD. The WMV approach combines the predictions from base-classifiers and chooses the class with greatest vote based on assigned weights to each classifier. A novel feature extraction method is used along the retinal pigment epithelium (RPE) layer, with the number of windows calculated for each picture playing an important part in identifying Dry-AMD/normal images using the WMV methodology. Pre-processing using hybrid-median filter followed by scale-invariant feature transform based segmentation of RPE layer and curvature flattening of retina is employed to measure exact thickness of RPE layer. RESULT The proposed model is trained on 70% of the OCT image database (OCTID) and evaluated on remaining OCTID and SD-OCT Noor dataset. Model has achieved accuracy of 96.15% and 96.94%, respectively. The suggested algorithm's effectiveness in Dry-AMD identification is demonstrated by comparison with alternative approaches. Even though the suggested model is only trained on the OCTID, it has performed well when tested on additional dataset. CONCLUSION The suggested architecture can be used for quick eye-screening for early identification of Dry-AMD. The recommended method may be applied in real-time since it requires fewer complexity and learning-variables.
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Affiliation(s)
- Moumita Sahoo
- Department of Applied Electronics and Instrumentation Engineering, Haldia Institute of Technology, Haldia, West Bengal, India.
| | - Madhuchhanda Mitra
- Department of Applied Physics, University of Calcutta, Kolkata, West Bengal, India
| | - Saurabh Pal
- Department of Applied Physics, University of Calcutta, Kolkata, West Bengal, India
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13
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Feng H, Chen J, Zhang Z, Lou Y, Zhang S, Yang W. A bibliometric analysis of artificial intelligence applications in macular edema: exploring research hotspots and Frontiers. Front Cell Dev Biol 2023; 11:1174936. [PMID: 37255600 PMCID: PMC10225517 DOI: 10.3389/fcell.2023.1174936] [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: 04/14/2023] [Accepted: 05/02/2023] [Indexed: 06/01/2023] Open
Abstract
Background: Artificial intelligence (AI) is used in ophthalmological disease screening and diagnostics, medical image diagnostics, and predicting late-disease progression rates. We reviewed all AI publications associated with macular edema (ME) research Between 2011 and 2022 and performed modeling, quantitative, and qualitative investigations. Methods: On 1st February 2023, we screened the Web of Science Core Collection for AI applications related to ME, from which 297 studies were identified and analyzed (2011-2022). We collected information on: publications, institutions, country/region, keywords, journal name, references, and research hotspots. Literature clustering networks and Frontier knowledge bases were investigated using bibliometrix-BiblioShiny, VOSviewer, and CiteSpace bibliometric platforms. We used the R "bibliometrix" package to synopsize our observations, enumerate keywords, visualize collaboration networks between countries/regions, and generate a topic trends plot. VOSviewer was used to examine cooperation between institutions and identify citation relationships between journals. We used CiteSpace to identify clustering keywords over the timeline and identify keywords with the strongest citation bursts. Results: In total, 47 countries published AI studies related to ME; the United States had the highest H-index, thus the greatest influence. China and the United States cooperated most closely between all countries. Also, 613 institutions generated publications - the Medical University of Vienna had the highest number of studies. This publication record and H-index meant the university was the most influential in the ME field. Reference clusters were also categorized into 10 headings: retinal Optical Coherence Tomography (OCT) fluid detection, convolutional network models, deep learning (DL)-based single-shot predictions, retinal vascular disease, diabetic retinopathy (DR), convolutional neural networks (CNNs), automated macular pathology diagnosis, dry age-related macular degeneration (DARMD), class weight, and advanced DL architecture systems. Frontier keywords were represented by diabetic macular edema (DME) (2021-2022). Conclusion: Our review of the AI-related ME literature was comprehensive, systematic, and objective, and identified future trends and current hotspots. With increased DL outputs, the ME research focus has gradually shifted from manual ME examinations to automatic ME detection and associated symptoms. In this review, we present a comprehensive and dynamic overview of AI in ME and identify future research areas.
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Affiliation(s)
- Haiwen Feng
- Department of Software Engineering, School of Software, Shenyang University of Technology, Shenyang, Liaoning, China
| | - Jiaqi Chen
- Department of Software Engineering, School of Software, Shenyang University of Technology, Shenyang, Liaoning, China
| | - Zhichang Zhang
- Department of Computer, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Yan Lou
- Department of Computer, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Shaochong Zhang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Weihua Yang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen, China
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14
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He J, Wang J, Han Z, Ma J, Wang C, Qi M. An interpretable transformer network for the retinal disease classification using optical coherence tomography. Sci Rep 2023; 13:3637. [PMID: 36869160 PMCID: PMC9984386 DOI: 10.1038/s41598-023-30853-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 03/02/2023] [Indexed: 03/05/2023] Open
Abstract
Retinal illnesses such as age-related macular degeneration and diabetic macular edema will lead to irreversible blindness. With optical coherence tomography (OCT), doctors are able to see cross-sections of the retinal layers and provide patients with a diagnosis. Manual reading of OCT images is time-consuming, labor-intensive and even error-prone. Computer-aided diagnosis algorithms improve efficiency by automatically analyzing and diagnosing retinal OCT images. However, the accuracy and interpretability of these algorithms can be further improved through effective feature extraction, loss optimization and visualization analysis. In this paper, we propose an interpretable Swin-Poly Transformer network for performing automatically retinal OCT image classification. By shifting the window partition, the Swin-Poly Transformer constructs connections between neighboring non-overlapping windows in the previous layer and thus has the flexibility to model multi-scale features. Besides, the Swin-Poly Transformer modifies the importance of polynomial bases to refine cross entropy for better retinal OCT image classification. In addition, the proposed method also provides confidence score maps, assisting medical practitioners to understand the models' decision-making process. Experiments in OCT2017 and OCT-C8 reveal that the proposed method outperforms both the convolutional neural network approach and ViT, with an accuracy of 99.80% and an AUC of 99.99%.
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Affiliation(s)
- Jingzhen He
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, China.
| | - Junxia Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
| | - Zeyu Han
- School of Mathematics and Statistics, Shandong University, Weihai, 264209, China
| | - Jun Ma
- School of Cyber Science and Engineering, Southeast University, Nanjing, 211189, China
| | - Chongjing Wang
- China Academy of Information and Communications Technology, Beijing, 100191, China
| | - Meng Qi
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China.
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15
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Integrating convolutional neural networks, kNN, and Bayesian optimization for efficient diagnosis of Alzheimer's disease in magnetic resonance images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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16
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Pavithra K, Kumar P, Geetha M, Bhandary SV. Computer aided diagnosis of diabetic macular edema in retinal fundus and OCT images: A review. Biocybern Biomed Eng 2023. [DOI: 10.1016/j.bbe.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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17
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Li HY, Wang DX, Dong L, Wei WB. Deep learning algorithms for detection of diabetic macular edema in OCT images: A systematic review and meta-analysis. Eur J Ophthalmol 2023; 33:278-290. [PMID: 35473414 DOI: 10.1177/11206721221094786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
PURPOSE Artificial intelligence (AI) can detect diabetic macular edema (DME) from optical coherence tomography (OCT) images. We aimed to evaluate the performance of deep learning neural networks in DME detection. METHODS Embase, Pubmed, the Cochrane Library, and IEEE Xplore were searched up to August 14, 2021. We included studies using deep learning algorithms to detect DME from OCT images. Two reviewers extracted the data independently, and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to assess the risk of bias. The study is reported according to Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA). RESULTS Ninteen studies involving 41005 subjects were included. The pooled sensitivity and specificity were 96.0% (95% confidence interval (CI): 93.9% to 97.3%) and 99.3% (95% CI: 98.2% to 99.7%), respectively. Subgroup analyses found that data set selection, sample size of training set and the choice of OCT devices contributed to the heterogeneity (all P < 0.05). While there was no association between the diagnostic accuracy and transfer learning adoption or image management (all P > 0.05). CONCLUSIONS Deep learning methods, particularly the convolutional neural networks (CNNs) could effectively detect clinically significant DME, which can provide referral suggestions to the patients.
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Affiliation(s)
- He-Yan Li
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, 117902Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Dai-Xi Wang
- 12517Capital Medical University, Beijing, China
| | - Li Dong
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, 117902Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Wen-Bin Wei
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, 117902Beijing Tongren Hospital, Capital Medical University, Beijing, China
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18
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Zhang X, Xiao Z, Li X, Wu X, Sun H, Yuan J, Higashita R, Liu J. Mixed pyramid attention network for nuclear cataract classification based on anterior segment OCT images. Health Inf Sci Syst 2022; 10:3. [PMID: 35401971 PMCID: PMC8956780 DOI: 10.1007/s13755-022-00170-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/04/2022] [Indexed: 11/25/2022] Open
Abstract
Nuclear cataract (NC) is a leading ocular disease globally for blindness and vision impairment. NC patients can improve their vision through cataract surgery or slow the opacity development with early intervention. Anterior segment optical coherence tomography (AS-OCT) image is an emerging ophthalmic image type, which can clearly observe the whole lens structure. Recently, clinicians have been increasingly studying the correlation between NC severity levels and clinical features from the nucleus region on AS-OCT images, and the results suggested the correlation is strong. However, automatic NC classification research based on AS-OCT images has rarely been studied. This paper presents a novel mixed pyramid attention network (MPANet) to classify NC severity levels on AS-OCT images automatically. In the MPANet, we design a novel mixed pyramid attention (MPA) block, which first applies the group convolution method to enhance the feature representation difference of feature maps and then construct a mixed pyramid pooling structure to extract local-global feature representations and different feature representation types simultaneously. We conduct extensive experiments on a clinical AS-OCT image dataset and a public OCT dataset to evaluate the effectiveness of our method. The results demonstrate that our method achieves competitive classification performance through comparisons to state-of-the-art methods and previous works. Moreover, this paper also uses the class activation mapping (CAM) technique to improve our method's interpretability of classification results.
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Affiliation(s)
- Xiaoqing Zhang
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, 518055 China
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055 China
| | - Zunjie Xiao
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055 China
| | - Xiaoling Li
- School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, 325035 China
| | - Xiao Wu
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055 China
| | - Hanxi Sun
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055 China
| | - Jin Yuan
- State Key Laboratory of Ophthalmology, Sun Yat-sen University, Guangzhou, 510060 China
| | - Risa Higashita
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055 China
- Present Address: Tomey Corporation, Nagoya, Japan
| | - Jiang Liu
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, 518055 China
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055 China
- School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, 325035 China
- Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, 518055 China
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Zhang X, Xiao Z, Hu L, Xu G, Higashita R, Chen W, Yuan J, Liu J. CCA-Net: Clinical-awareness attention network for nuclear cataract classification in AS-OCT. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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20
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Ara RK, Matiolański A, Dziech A, Baran R, Domin P, Wieczorkiewicz A. Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach. SENSORS (BASEL, SWITZERLAND) 2022; 22:4675. [PMID: 35808169 PMCID: PMC9269557 DOI: 10.3390/s22134675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/13/2022] [Accepted: 06/16/2022] [Indexed: 05/18/2023]
Abstract
The use of optical coherence tomography (OCT) in medical diagnostics is now common. The growing amount of data leads us to propose an automated support system for medical staff. The key part of the system is a classification algorithm developed with modern machine learning techniques. The main contribution is to present a new approach for the classification of eye diseases using the convolutional neural network model. The research concerns the classification of patients on the basis of OCT B-scans into one of four categories: Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), Drusen, and Normal. Those categories are available in a publicly available dataset of above 84,000 images utilized for the research. After several tested architectures, our 5-layer neural network gives us a promising result. We compared them to the other available solutions which proves the high quality of our algorithm. Equally important for the application of the algorithm is the computational time, which is reduced by the limited size of the model. In addition, the article presents a detailed method of image data augmentation and its impact on the classification results. The results of the experiments were also presented for several derived models of convolutional network architectures that were tested during the research. Improving processes in medical treatment is important. The algorithm cannot replace a doctor but, for example, can be a valuable tool for speeding up the process of diagnosis during screening tests.
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Affiliation(s)
- Rouhollah Kian Ara
- Institute of Telecommunications, AGH University of Science and Technology, 30-059 Krakow, Poland; (R.K.A.); (A.D.)
| | - Andrzej Matiolański
- Institute of Telecommunications, AGH University of Science and Technology, 30-059 Krakow, Poland; (R.K.A.); (A.D.)
| | - Andrzej Dziech
- Institute of Telecommunications, AGH University of Science and Technology, 30-059 Krakow, Poland; (R.K.A.); (A.D.)
| | - Remigiusz Baran
- Faculty of Electrical Engineering, Automatic Control and Computer Science, Kielce University of Technology, 25-314 Kielce, Poland;
| | - Paweł Domin
- Consultronix S.A., 32-083 Balice, Poland; (P.D.); (A.W.)
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21
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Mou L, Liang L, Gao Z, Wang X. A multi-scale anomaly detection framework for retinal OCT images based on the Bayesian neural network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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22
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Attention to region: Region-based integration-and-recalibration networks for nuclear cataract classification using AS-OCT images. Med Image Anal 2022; 80:102499. [DOI: 10.1016/j.media.2022.102499] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/31/2022] [Accepted: 05/24/2022] [Indexed: 01/16/2023]
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23
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Sotoudeh-Paima S, Jodeiri A, Hajizadeh F, Soltanian-Zadeh H. Multi-scale convolutional neural network for automated AMD classification using retinal OCT images. Comput Biol Med 2022; 144:105368. [DOI: 10.1016/j.compbiomed.2022.105368] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 02/28/2022] [Accepted: 02/28/2022] [Indexed: 11/29/2022]
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24
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One-dimensional convolutional neural network architecture for classification of mental tasks from electroencephalogram. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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25
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Wong PK, Yan T, Wang H, Chan IN, Wang J, Li Y, Ren H, Wong CH. Automatic detection of multiple types of pneumonia: Open dataset and a multi-scale attention network. Biomed Signal Process Control 2022; 73:103415. [PMID: 34909050 PMCID: PMC8660060 DOI: 10.1016/j.bspc.2021.103415] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 10/31/2021] [Accepted: 11/28/2021] [Indexed: 12/13/2022]
Abstract
The quick and precise identification of COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, mycoplasma pneumonia, and normal lung on chest CT images play a crucial role in timely quarantine and medical treatment. However, manual identification is subject to potential misinterpretations and time-consumption issues owing the visual similarities of pneumonia lesions. In this study, we propose a novel multi-scale attention network (MSANet) based on a bag of advanced deep learning techniques for the automatic classification of COVID-19 and multiple types of pneumonia. The proposed method can automatically pay attention to discriminative information and multi-scale features of pneumonia lesions for better classification. The experimental results show that the proposed MSANet can achieve an overall precision of 97.31%, recall of 96.18%, F1-score of 96.71%, accuracy of 97.46%, and macro-average area under the receiver operating characteristic curve (AUC) of 0.9981 to distinguish between multiple classes of pneumonia. These promising results indicate that the proposed method can significantly assist physicians and radiologists in medical diagnosis. The dataset is publicly available at https://doi.org/10.17632/rf8x3wp6ss.1.
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Affiliation(s)
- Pak Kin Wong
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau
| | - Tao Yan
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, China
| | - Huaqiao Wang
- Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
| | - In Neng Chan
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau
| | - Jiangtao Wang
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, China
| | - Yang Li
- Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
| | - Hao Ren
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, China
| | - Chi Hong Wong
- Faculty of Medicine, Macau University of Science and Technology, Taipa 999078, Macau
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Zhang X, Xiao Z, Higashita R, Hu Y, Chen W, Yuan J, Liu J. Adaptive feature squeeze network for nuclear cataract classification in AS-OCT image. J Biomed Inform 2022; 128:104037. [PMID: 35245700 DOI: 10.1016/j.jbi.2022.104037] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 01/24/2022] [Accepted: 02/21/2022] [Indexed: 10/19/2022]
Abstract
Nuclear cataract (NC) is an age-related cataract disease. Cataract surgery is an effective method to improve the vision and life quality of NC patients. Anterior segment optical coherence tomography (AS-OCT) images are noninvasive, reproductive, and easy-measured, which can capture opacity clearly on the lens nucleus region. However, automatic AS-OCT-based NC classification research has not been extensively studied. This paper proposes a novel convolutional neural network (CNN) framework named Adaptive Feature Squeeze Network (AFSNet) to classify NC severity levels automatically. In the AFSNet, we construct an adaptive feature squeeze module to dynamically squeeze local feature representations and update the relative importance of global feature representations, which is comprised of a squeeze block and a global adaptive pooling operation. We conduct comprehensive experiments on a clinical AS-OCT image dataset and a public OCT images dataset, and results demonstrate our method's effectiveness and superiority over strong baselines and previous state-of-the-art methods. Furthermore, this paper also demonstrates that CNNs achieve better NC classification results on the nucleus region than the lens region. We also adopt the class activation mapping (CAM) technique to localize the discriminative regions that CNN models learned, which enhances the interpretability of classification results.
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Affiliation(s)
- Xiaoqing Zhang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Zunjie Xiao
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | | | - Yan Hu
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Wan Chen
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Jin Yuan
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Jiang Liu
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China; Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China; Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China; Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China
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27
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Automatic Detection of Age-Related Macular Degeneration Based on Deep Learning and Local Outlier Factor Algorithm. Diagnostics (Basel) 2022; 12:diagnostics12020532. [PMID: 35204621 PMCID: PMC8871377 DOI: 10.3390/diagnostics12020532] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/13/2022] [Accepted: 02/17/2022] [Indexed: 02/06/2023] Open
Abstract
Age-related macular degeneration (AMD) is a retinal disorder affecting the elderly, and society’s aging population means that the disease is becoming increasingly prevalent. The vision in patients with early AMD is usually unaffected or nearly normal but central vision may be weakened or even lost if timely treatment is not performed. Therefore, early diagnosis is particularly important to prevent the further exacerbation of AMD. This paper proposed a novel automatic detection method of AMD from optical coherence tomography (OCT) images based on deep learning and a local outlier factor (LOF) algorithm. A ResNet-50 model with L2-constrained softmax loss was retrained to extract features from OCT images and the LOF algorithm was used as the classifier. The proposed method was trained on the UCSD dataset and tested on both the UCSD dataset and Duke dataset, with an accuracy of 99.87% and 97.56%, respectively. Even though the model was only trained on the UCSD dataset, it obtained good detection accuracy when tested on another dataset. Comparison with other methods also indicates the efficiency of the proposed method in detecting AMD.
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28
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Barua PD, Chan WY, Dogan S, Baygin M, Tuncer T, Ciaccio EJ, Islam N, Cheong KH, Shahid ZS, Acharya UR. Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1651. [PMID: 34945957 PMCID: PMC8700736 DOI: 10.3390/e23121651] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/22/2021] [Accepted: 11/25/2021] [Indexed: 01/04/2023]
Abstract
Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model.
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Affiliation(s)
- Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia;
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Wai Yee Chan
- University Malaya Research Imaging Centre, Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 59100, Malaysia;
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23002, Turkey; (S.D.); (T.T.)
| | - Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan 75000, Turkey;
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23002, Turkey; (S.D.); (T.T.)
| | - Edward J. Ciaccio
- Department of Medicine, Columbia University Irving Medical Center, New York, NY 10032-3784, USA;
| | - Nazrul Islam
- Glaucoma Faculty, Bangladesh Eye Hospital & Institute, Dhaka 1206, Bangladesh;
| | - Kang Hao Cheong
- Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore 487372, Singapore
| | - Zakia Sultana Shahid
- Department of Ophthalmology, Anwer Khan Modern Medical College, Dhaka 1205, Bangladesh;
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 129799, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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Zhang Y, Li M, Ji Z, Fan W, Yuan S, Liu Q, Chen Q. Twin self-supervision based semi-supervised learning (TS-SSL): Retinal anomaly classification in SD-OCT images. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.08.051] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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30
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Thomas A, Harikrishnan PM, Ramachandran R, Ramachandran S, Manoj R, Palanisamy P, Gopi VP. A novel multiscale and multipath convolutional neural network based age-related macular degeneration detection using OCT images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 209:106294. [PMID: 34364184 DOI: 10.1016/j.cmpb.2021.106294] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 07/15/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE One of the significant retinal diseases that affected older people is called Age-related Macular Degeneration (AMD). The first stage creates a blur effect on vision and later leads to central vision loss. Most people overlooked the primary stage blurring and converted it into an advanced stage. There is no proper treatment to cure the disease. So the early detection of AMD is essential to prevent its extension into the advanced stage. This paper proposes a novel deep Convolutional Neural Network (CNN) architecture to automate AMD diagnosis early from Optical Coherence Tomographic (OCT) images. METHODS The proposed architecture is a multiscale and multipath CNN with six convolutional layers. The multiscale convolution layer permits the network to produce many local structures with various filter dimensions. The multipath feature extraction permits CNN to merge more features regarding the sparse local and fine global structures. The performance of the proposed architecture is evaluated through ten-fold cross-validation methods using different classifiers like support vector machine, multi-layer perceptron, and random forest. RESULTS The proposed CNN with the random forest classifier gives the best classification accuracy results. The proposed method is tested on data set 1, data set 2, data set 3, data set 4, and achieved an accuracy of 0.9666, 0.9897, 0.9974, and 0.9978 respectively, with random forest classifier. Also, we tested the combination of first three data sets and achieved an accuracy of 0.9902. CONCLUSIONS An efficient algorithm for detecting AMD from OCT images is proposed based on a multiscale and multipath CNN architecture. Comparison with other approaches produced results that exhibit the efficiency of the proposed algorithm in the detection of AMD. The proposed architecture can be applied in rapid screening of the eye for the early detection of AMD. Due to less complexity and fewer learnable parameters.
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Affiliation(s)
- Anju Thomas
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu 620015, India.
| | - P M Harikrishnan
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu 620015, India.
| | - Rajiv Ramachandran
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu 620015, India.
| | - Srikkanth Ramachandran
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu 620015, India.
| | - Rigved Manoj
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu 620015, India.
| | - P Palanisamy
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu 620015, India.
| | - Varun P Gopi
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu 620015, India.
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CDC-Net: Cascaded decoupled convolutional network for lesion-assisted detection and grading of retinopathy using optical coherence tomography (OCT) scans. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103030] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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32
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Arefin R, Samad MD, Akyelken FA, Davanian A. Non-transfer Deep Learning of Optical Coherence Tomography for Post-hoc Explanation of Macular Disease Classification. IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS. IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS 2021; 2021:48-52. [PMID: 36168324 PMCID: PMC9511893 DOI: 10.1109/ichi52183.2021.00020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Deep transfer learning is a popular choice for classifying monochromatic medical images using models that are pretrained by natural images with color channels. This choice may introduce unnecessarily redundant model complexity that can limit explanations of such model behavior and outcomes in the context of medical imaging. To investigate this hypothesis, we develop a configurable deep convolutional neural network (CNN) to classify four macular disease conditions using retinal optical coherence tomography (OCT) images. Our proposed non-transfer deep CNN model (acc: 97.9%) outperforms existing transfer learning models such as ResNet-50 (acc: 89.0%), ResNet-101 (acc: 96.7%), VGG-19 (acc: 93.3%), Inception-V3 (acc: 95.8%) in the same retinal OCT image classification task. We perform post-hoc analysis of the trained model and model extracted image features, which reveals that only eight out of 256 filter kernels are active at our final convolutional layer. The convolutional responses of these selective eight filters yield image features that efficiently separate four macular disease classes even when projected onto two-dimensional principal component space. Our findings suggest that many deep learning parameters and their computations are redundant and expensive for retinal OCT image classification, which are expected to be more intense when using transfer learning. Additionally, we provide clinical interpretations of our misclassified test images identifying manifest artifacts, shadowing of useful texture, false texture representing fluids, and other confounding factors. These clinical explanations along with model optimization via kernel selection can improve the classification accuracy, computational costs, and explainability of model outcomes.
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Affiliation(s)
- Raisul Arefin
- Dept. of Computer Science Auburn University Auburn, AB USA
| | - Manar D Samad
- Dept. of Computer Science Tennessee State University Nashville, TN USA
| | - Furkan A Akyelken
- Dept. of Computer Science Tennessee State University Nashville, TN, USA
| | - Arash Davanian
- Vanderbilt Eye Institute Vanderbilt University Medical Center Nashville, TN, USA
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34
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Thomas A, P. M. H, K. Krishna A, P. P, Gopi VP. A novel multiscale convolutional neural network based age-related macular degeneration detection using OCT images. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102538] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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35
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A P S, Kar S, S G, Gopi VP, Palanisamy P. OctNET: A Lightweight CNN for Retinal Disease Classification from Optical Coherence Tomography Images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105877. [PMID: 33339630 DOI: 10.1016/j.cmpb.2020.105877] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 11/22/2020] [Indexed: 05/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Retinal diseases are becoming a major health problem in recent years. Their early detection and ensuing treatment are essential to prevent visual damage, as the number of people affected by diabetes is expected to grow exponentially. Retinal diseases progress slowly, without any discernible symptoms. Optical Coherence Tomography (OCT) is a diagnostic tool capable of analyzing and identifying the quantitative discrimination in the disease affected retinal layers with high resolution. This paper proposes a deep neural network-based classifier for the computer-aided classification of Diabetic Macular Edema (DME), drusen, Choroidal NeoVascularization (CNV) from normal OCT images of the retina. METHODS In the proposed method, we demonstrate the feasibility of classifying and detecting severe retinal pathologies from OCT images using a deep convolutional neural network having six convolutional blocks. The classification results are explained using a gradient-based class activation mapping algorithm. RESULTS Training and validation of the model are performed on a public dataset of 83,484 images with expert-level disease grading of CNV, DME, and drusen, in addition to normal retinal image. We achieved a precision of 99.69%, recall of 99.69%, and accuracy of 99.69% with only three misclassifications out of 968 test cases. CONCLUSION In the proposed work, downsampling and weight sharing were introduced to improve the training efficiency and were found to reduce the trainable parameters significantly. The class activation mapping was also performed, and the output image was similar to the retina's actual color OCT images. The proposed network used only 6.9% of learnable parameters compared to the existing ResNet-50 model and yet outperformed it in classification. The proposed work can be potentially employed in real-time applications due to reduced complexity and fewer learnable parameters over other models.
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Affiliation(s)
- Sunija A P
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu-620015, India.
| | - Saikat Kar
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu-620015, India.
| | - Gayathri S
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu-620015, India.
| | - Varun P Gopi
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu-620015, India.
| | - P Palanisamy
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu-620015, India.
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