Transfer learning for Diabetic Macular Edema (DME) detection on Optical Coherence Tomography (OCT) images

Chan, Genevieve C. Y.; Awais, Muhammad; Shah, Syed Ayaz Ali; Tang, Tong Boon; Lu, Cheng-Kai; Mériaudeau, Fabrice

doi:10.1109/icsipa.2017.8120662

Cited by 41 publications

(27 citation statements)

References 10 publications

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“…6 , 7 Moreover, the VGG16 and InceptionV3 have been enormously applied to assist medical image identification, such as taxonomy of the CT image with lung cancer to classify the pathological types, 8 to identify the endoscopy images with different lesions, 9 and assisted ophthalmologist to analyze the variation of the OCT images. 10 , 11 …”

Section: Discussionmentioning

confidence: 99%

Optical coherence tomography–based diabetic macula edema screening with artificial intelligence

Hwang

Chou

Lin

et al. 2020

Journal of the Chinese Medical Association

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Background: Optical coherence tomography (OCT) is considered as a sensitive and noninvasive tool to evaluate the macular lesions. In patients with diabetes mellitus (DM), the existence of diabetic macular edema (DME) can cause significant vision impairment and further intravitreal injection (IVI) of anti–vascular endothelial growth factor (VEGF) is needed. However, the increasing number of DM patients makes it a big burden for clinicians to manually determine whether DME exists in the OCT images. The artificial intelligence (AI) now enormously applied to many medical territories may help reduce the burden on clinicians. Methods: We selected DME patients receiving IVI of anti-VEGF or corticosteroid at Taipei Veterans General Hospital in 2017. All macular cross-sectional scan OCT images were collected retrospectively from the eyes of these patients from January 2008 to July 2018. We further established AI models based on convolutional neural network architecture to determine whether the DM patients have DME by OCT images. Results: Based on the convolutional neural networks, InceptionV3 and VGG16, our AI system achieved a high DME diagnostic accuracy of 93.09% and 92.82%, respectively. The sensitivity of the VGG16 and InceptionV3 models was 96.48% and 95.15%., respectively. The specificity was corresponding to 86.67% and 89.63% for VGG16 and InceptionV3, respectively. We further developed an OCT-driven platform based on these AI models. Conclusion: We successfully set up AI models to provide an accurate diagnosis of DME by OCT images. These models may assist clinicians in screening DME in DM patients in the future.

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Section: Discussionmentioning

confidence: 99%

Optical coherence tomography–based diabetic macula edema screening with artificial intelligence

Hwang

Chou

Lin

et al. 2020

Journal of the Chinese Medical Association

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“…46 Genevieve C. Y. Chan et al used AlexNet and SVM to diagnosis DME. 47 Muhammad Awais et al used pre-trained VGG16 model to detect DME. 48 Thomas Schlegl et al used convolutional neural network with an encoder-decoder architecture to distinguish the three eye diseases, 49 as shown in Table 1.…”

Section: Discussionmentioning

confidence: 99%

<p>Identifying Diabetic Macular Edema and Other Retinal Diseases by Optical Coherence Tomography Image and Multiscale Deep Learning</p>

Zhang¹,

Liu²,

Li³

et al. 2020

DMSO

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Purpose: Diabetic Macular Edema has been one of the research hotspots all over the world. But as the global population continues to grow, the number of OCT images requiring manual analysis is becoming increasingly unaffordable. Medical images are often fuzzy due to the inherent physical processes of acquiring them. It is difficult for traditional algorithms to use low-quality data. And traditional algorithms usually only provide diagnostic results, which makes the reliability and interpretability of the model face challenges. To solve problem above, we proposed a more intuitive and robust diagnosis model with self-enhancement ability and clinical triage patients' ability. Methods: We used 38,057 OCT images (Drusen, DME, CNV and Normal) to establish and evaluate the model. All data are OCT images of fundus retina. There were 37,457 samples in the training dataset and 600 samples in the validation dataset. In order to diagnose these images accurately, we propose a multiscale transfer learning algorithm. Firstly, the sample is sent to the automatic self-enhancement module for edge detection and enhancement. Then, the processed data are sent to the image diagnosis module to determine the disease type. This process makes more data more effective and can be accurately classified. Finally, we calculated the accuracy, precision, sensitivity and specificity of the model, and verified the performance of the model from the perspective of clinical application. Results: The model proposed in this paper can provide the diagnosis results and display the detection targets more intuitively. The model reached 94.5% accuracy, 97.2% precision, 97.7% sensitivity and 97% specificity in the independent testing dataset. Conclusion: Comparing the performance of relevant work and ablation test, our model achieved relatively good performance. It is proved that the model proposed in this paper has a stronger ability to recognize diseases even in the face of low-quality images. Experiment results also demonstrate its clinical referral capability. It can reduce the workload of medical staff and save the precious time of patients.

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“…To completely train a blank deep CNN, a large data set is needed in which millions of weight has to be adjusted [25, 26]. As the current data is not sufficient to design a new network, using transfer learning, a CNN in which the features are previously learned is used for the OCT image classification [27]. Transfer learning is implemented through MATLAB software with the following key points.…”

Section: Development Of An Intelligent System For Dme Categorisationmentioning

confidence: 99%

Automated detection of multiple structural changes of diabetic macular oedema in SDOCT retinal images through transfer learning in CNNs

Padmasini

Umamaheswari²

2020

IET image process

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Diabetic Maculopathy, the major cause of vision loss, occurs due to uncontrolled diabetes. This affects the retinal layers of the eye causing bleeding of vessels, which results in Diabetic Macular Edema (DME), macular detachment etc., Basically three structural changes are involved in DME viz., Cystoid Macular Edema (CME), Serous Macular Detachment (SMD) and Intra Retinal Fluid (IRF). These changes may also coexist with each other such as CME with SMD or CME with IRF etc. In this work, the retinal images acquired through Spectral Domain Optical Coherence Tomography (SDOCT) imaging modality, which would provide high level of precision and resolution of the retinal layers are utilized. An automated algorithm to detect and differentiate seven types of DME based on deep learning is implemented using transfer learning on three Convolutional Neural Networks (CNNs), ResNet 50, VGGNet and AlexNet. In detection of DME, ResNet 50 performs excellently well, when compared with Alexnet and VGGNet, because of its depth and skip connection features. The average values of the statistical parameters such as accuracy (0.993), F1 score (0.975), Mathews Correlation Coefficient(MCC)(0.972) of ResNet are high when compared to that of AlexNet and VGGNet

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Transfer learning for Diabetic Macular Edema (DME) detection on Optical Coherence Tomography (OCT) images

Cited by 41 publications

References 10 publications

Optical coherence tomography–based diabetic macula edema screening with artificial intelligence

Optical coherence tomography–based diabetic macula edema screening with artificial intelligence

<p>Identifying Diabetic Macular Edema and Other Retinal Diseases by Optical Coherence Tomography Image and Multiscale Deep Learning</p>

Automated detection of multiple structural changes of diabetic macular oedema in SDOCT retinal images through transfer learning in CNNs

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