Automated Diabetic Macular Edema (DME) Analysis using Fine Tuning with Inception-Resnet-v2 on OCT Images

Kamble, Ravi; Chan, Genevieve C. Y.; Perdomo, Oscar J.; Kokare, Manesh; González, Fabio A.; Müller, Henning; Mériaudeau, Fabrice

doi:10.1109/iecbes.2018.8626616

Cited by 50 publications

(24 citation statements)

References 14 publications

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“…The proposed model achieved a classification accuracy of 98.33% in 31.13 seconds. Kamble et al [44] suggested an approach by using the convolutional neural network with fine-tuning for automatic detection for diabetic macular edema versus the normal cases on OCT images. The Authors use the effective fine-tuning method with Inception-Resnet-V2 CNN and tested the proposed model on Singapore eye research institute and Chinese university Hong-Kong datasets which are accessible publically.…”

Section: Technique Based Analysis Of Ophthalmologymentioning

confidence: 99%

Deep Learning Based Analysis of Ophthalmology: A Systematic Review

Kumar¹,

Kumar²,

Gupta³

2021

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Diagnosis of medical-related problems is the biggest issue in every era. In past decades, due to less number of technologies and equipment's it was challenging to diagnose it. As time passes, technologies i.e. Artificial Intelligence (AI) grow and became popular in the medical field especially in ophthalmology at proliferating rate. But, still, many of the diseases are diagnose manually. (i.e., eye disease disorder) which is time consuming, expensive, and tedious task. Existing prediction systems can resolve medical issues such as ocular disorder but the accuracy of prediction is very less. OBJECTIVES: This study gives a brief overview of the analysis of traditional systems with modern approaches. Further, this study highlights the different allied techniques and impact of transfer learning on ophthalmology for the detection of various eye diseases i.e., Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), Glaucoma, etc. METHODS: AI and Machine Learning Technique are used to conduct this research for analysis. RESULTS: The result of this paper concludes AI with allied techniques may reshape and revolutionize the medical community especially in the area of ophthalmology. CONCLUSION: This paper presented a comprehensive review of AI with allied techniques in ophthalmology. In ML and DL-based approaches, CNN provides the most promising results.

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Section: Technique Based Analysis Of Ophthalmologymentioning

confidence: 99%

Deep Learning Based Analysis of Ophthalmology: A Systematic Review

Kumar¹,

Kumar²,

Gupta³

2021

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

show abstract

“…The types of CNNs network used varies between different applications. For example, a pre-trained CNN network of VGG16 [15] and, InceptionResNet-V2, Inception-V3 and, ResNet-50 architecture [16] had used to detect diabetic macular edema (DME). AlexNet, VGG19 and, Inception-V3 architecture had used to detect kawasaki disease in optical coherence tomography (OCT) images [17].…”

Section: Introductionmentioning

confidence: 99%

Calcification detection for intravascular ultrasound image using direct acyclic graph architecture: pre-trained model for 1-channel image

Sofian

Than

Mohamad

et al. 2021

IJEECS

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Coronary artery calcification is a calcium buildup within the walls of the arteries. It is considered a predominant marker for coronary artery disease. Thus many approaches have been developed for the automatic detection of calcification. The previous calcification detection was on segmentation of other structures as pre-processing steps or using the fact that the calcification often appears as a bright region. In this paper, an automated system proposed using a deep learning approach to detect the calcification absence and calcification presence in coronary artery IVUS image. A useful advantage of deep learning, compared to other methods is, it uses representations and features directly from the raw data, bypassing the need to manually extract features, a common that required in the traditional machine learning framework. The type of deep learning architecture used is 27 layers of convolutional neural networks (CNNs) using Direct Acyclic Graph. The proposed system used 2175 images and achieved an accuracy of 98.16% for Cartesian coordinate images and 99.08% for Polar Reconstructed Coordinate images.

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“…Several works on macular OCT image classification using CNN models have been conducted. 8 – 13 The attention mechanism in deep learning is similar to the attention mechanism of human vision in that it focuses attention on important points among a large number of information, selecting key information and ignoring other unimportant information. In OCT image classification, attention could focus on lesion part, which usually occupies only a very small part of the OCT scan, and it has been explored and introduced for macular OCT classification applications.…”

Section: Introductionmentioning

confidence: 99%

Automatic diagnosis of macular diseases from OCT volume based on its two-dimensional feature map and convolutional neural network with attention mechanism

2020

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Significance: Automatic and accurate classification of three-dimensional (3-D) retinal optical coherence tomography (OCT) images is essential for assisting ophthalmologist in the diagnosis and grading of macular diseases. Therefore, more effective OCT volume classification for automatic recognition of macular diseases is needed. Aim: For OCT volumes in which only OCT volume-level labels are known, OCT volume classifiers based on its global feature and deep learning are designed, validated, and compared with other methods. Approach: We present a general framework to classify OCT volume for automatic recognizing macular diseases. The architecture of the framework consists of three modules: B-scan feature extractor, two-dimensional (2-D) feature map generation, and volume-level classifier. Our architecture could address OCT volume classification using two 2-D image machine learning classification algorithms. Specifically, a convolutional neural network (CNN) model is trained and used as a B-scan feature extractor to construct a 2-D feature map of an OCT volume and volumelevel classifiers such as support vector machine and CNN with/without attention mechanism for 2-D feature maps are described. Results: Our proposed methods are validated on the publicly available Duke dataset, which consists of 269 intermediate age-related macular degeneration (AMD) volumes and 115 normal volumes. Fivefold cross-validation was done, and average accuracy, sensitivity, and specificity of 98.17%, 99.26%, and 95.65%, respectively, are achieved. The experiments show that our methods outperform the state-of-the-art methods. Our methods are also validated on our private clinical OCT volume dataset, consisting of 448 AMD volumes and 462 diabetic macular edema volumes. Conclusions: We present a general framework of OCT volume classification based on its 2-D feature map and CNN with attention mechanism and describe its implementation schemes. Our proposed methods could classify OCT volumes automatically and effectively with high accuracy, and they are a potential practical tool for screening of ophthalmic diseases from OCT volume.

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Automated Diabetic Macular Edema (DME) Analysis using Fine Tuning with Inception-Resnet-v2 on OCT Images

Cited by 50 publications

References 14 publications

Deep Learning Based Analysis of Ophthalmology: A Systematic Review

Deep Learning Based Analysis of Ophthalmology: A Systematic Review

Calcification detection for intravascular ultrasound image using direct acyclic graph architecture: pre-trained model for 1-channel image

Automatic diagnosis of macular diseases from OCT volume based on its two-dimensional feature map and convolutional neural network with attention mechanism

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