Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification

Hua, Cam-Hao; Huynh‐The, Thien; Kim, Ki‐Young; Yu, Seung-Young; Le-Tien, Thuong; Park, Gwang Hoon; Bang, Jae Hun; Khan, Wajahat Ali; Bae, Sung‐Ho; Lee, Sungyoung

doi:10.1016/j.ijmedinf.2019.07.005

Cited by 51 publications

(17 citation statements)

References 31 publications

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“…ophthalmologic fields, especially for the diagnosis, segmentation, classification, and staging of diseases. In recent years, there have been several recent studies on the prediction of disease progression, such as age-related macular degeneration (11), glaucoma(12,13), diabetic retinopathy(14,15), and other diseases(16,17), using deep learning of the actual images. We firstly…”

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confidence: 99%

Prediction of keratoconus progression using deep learning of anterior segment optical coherence tomography maps

Kamiya¹,

Ayatsuka²,

Kato³

et al. 2021

Ann Transl Med

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Background: To predict keratoconus progression using deep learning of the color-coded maps measured with a swept-source anterior segment optical coherence tomography (As-OCT) device. Methods:We enrolled 218 keratoconic eyes with and without disease progression. Using deep learning of the 6 color-coded maps (anterior elevation, anterior curvature, posterior elevation, posterior curvature, total refractive power, and pachymetry map) obtained by the As-OCT (CASIA, Tomey), we assessed the accuracy, sensitivity, and specificity of prediction of keratoconus progression in such eyes.Results: Deep learning of the 6 color-coded maps exhibited an accuracy of 0.794 in discriminating keratoconus with and without progression. For a single map analysis, posterior elevation map (0.798) showed the highest accuracy, followed by anterior curvature map (0.775), posterior corneal curvature map (0.757), anterior elevation map (0.752), total refractive power map (0.729), and pachymetry map (0.720), in distinguishing between progressive and non-progressive keratoconus. The use of the adjusted algorithm by age subgroups improved to an accuracy of 0.849.Conclusions: Deep learning of the As-OCT color-coded maps effectively discriminates progressive keratoconus from non-progressive keratoconus with an accuracy of approximately 85% using the adjusted age algorithm, indicating that it will become an aid for predicting the progression of the disease, which is clinically beneficial for decision-making of the surgical indication of corneal cross-linking (CXL).

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confidence: 99%

Prediction of keratoconus progression using deep learning of anterior segment optical coherence tomography maps

Kamiya¹,

Ayatsuka²,

Kato³

et al. 2021

Ann Transl Med

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“…In the scope of DL-based classification, Hua et al [ 83 ] designed a DL model named Trilogy of Skip-connection Deep Networks (Tri-SDN) over the pretrained base model ResNet50 that applies skip connection blocks to make the tuning faster yielding to ACC and SP of 90.6% and 82.1%, respectively, which is considerably better than the values of 83.3% and 64.1% compared with the situation when skip connection blocks are not used.…”

Section: Resultsmentioning

confidence: 99%

Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

et al. 2021

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Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this purpose, both fundus and optical coherence tomography (OCT) images are used to image the retina. Next, Deep-learning (DL)-/machine-learning (ML)-based approaches make it possible to extract features from the images and to detect the presence of DR, grade its severity and segment associated lesions. This review covers the literature dealing with AI approaches to DR such as ML and DL in classification and segmentation that have been published in the open literature within six years (2016–2021). In addition, a comprehensive list of available DR datasets is reported. This list was constructed using both the PICO (P-Patient, I-Intervention, C-Control, O-Outcome) and Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2009 search strategies. We summarize a total of 114 published articles which conformed to the scope of the review. In addition, a list of 43 major datasets is presented.

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“…Certain methods in this subclass have achieved a good trade-off between model accuracy and complexity by using advanced feature selection schemes and sophisticated ML algorithms [59]. • Innovative DL-based approaches: Inspired by great success in the fields of image processing and computer vision [62]- [64], the DL technique has been exploited for modulation classification, wherein several deep network architectures, such as RNN, long short-term memory (LSTM), and CNN, have been considered. Compared with traditional ML, DL has important advantages because it can automatically learn high-level features for more effective modulation discrimination and it can effectively process wireless big data [65].…”

Section: B State-of-the-art Amc Methodsmentioning

confidence: 99%

Intelligent Radio Signal Processing: A Survey

Pham

Nguyen

Huynh‐The

et al. 2020

Preprint

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Intelligent signal processing for wireless communications is a vital task in modern wireless systems, but it faces new challenges because of network heterogeneity, diverse service requirements, a massive number of connections, and various radio characteristics. Owing to recent advancements in big data and computing technologies, artificial intelligence (AI) has become a useful tool for radio signal processing and has enabled the realization of intelligent radio signal processing. This survey covers four intelligent signal processing topics for the wireless physical layer, including modulation classification, signal detection, beamforming, and channel estimation. In particular, each theme is presented in a dedicated section, starting with the most fundamental principles, followed by a review of up-to-date studies and a summary. To provide the necessary background, we first present a brief overview of AI techniques such as machine learning, deep learning, and federated learning. Finally, we highlight a number of research challenges and future directions in the area of intelligent radio signal processing. We expect this survey to be a good source of information for anyone interested in intelligent radio signal processing, and the perspectives we provide therein will stimulate many more novel ideas and contributions in the future.

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Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification

Cited by 51 publications

References 31 publications

Prediction of keratoconus progression using deep learning of anterior segment optical coherence tomography maps

Prediction of keratoconus progression using deep learning of anterior segment optical coherence tomography maps

Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

Intelligent Radio Signal Processing: A Survey

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