Automatic microaneurysms detection on retinal images using deep convolution neural network

Hatanaka, Yuji; Ogohara, Kazunori; Sunayama, Wataru; Miyashita, Mitsuhiro; Muramatsu, Chisako; Fujita, Hiroshi

doi:10.1109/iwait.2018.8369794

Cited by 18 publications

(10 citation statements)

References 7 publications

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“…Reducing the image means deleting pixels from columns and lines that have little effect on the image details. By running a python script via CMD with the dataset route, we resized Inception V- [20] Retinopathy of diabetic patients for detecting exudate CNN Chudzik et al [21] Interleaved freezing of deep learning method for microaneurysm detection Transfer learning and layer freezing Hatanaka et al [22] Retinal images are used to automatic microaneurysms detect using deep convolution neural network DCNN Dai et al [23] • Multi-sieving deep learning is used to detect retinal microaneurysm for clinical report CNN Saba et al [24] Glaucoma detection using fundus image A mixture of ML methods Fourcade et al [25] Image analysis for medical purposes using deep learning CNN Faes et al [26] Medical image classification using deep learning model design Google cloud AutoML Katzmann et al [27] Medical small-sized image data classification using RF algorithm Random forest classifiers and deep ensembles Smaida and Yaroshchak [28] Deep learning convolutional network based on keras and tensor flow using python for image classification DCNN Hameed et al [29] Eye diseases classification Back propagation with parabola learning rate additional data can best provide skilled models. e variations of the pictures can be created with this technology, improving the possibility of fitting in models to simplify what they have learned about the new pics.…”

Section: Methodsmentioning

confidence: 99%

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Digital Forensics Use Case for Glaucoma Detection Using Transfer Learning Based on Deep Convolutional Neural Networks

Latif

Xiao

et al. 2021

Security and Communication Networks

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In parallel with the development of various emerging fields such as computer vision and related technologies, e.g., iris identification and glaucoma detection, criminals are developing their methods. It is the foremost reason for the blindness of human beings that affects the eye’s optic nerve. Fundus photography is carried out to examine this eye disease. Medical experts evaluate fundus photographs, which is a time-consuming visual inspection. Most current systems for automated glaucoma detection in fundus images rely on segmentation-based features nuanced by the underlying segmentation methods. Convolutional neural networks (CNNs) are powerful tools for solving image classification tasks, as they can learn highly discriminative features from raw pixel intensities. However, their applicability to medical image analysis is limited by the nonavailability of large sets of annotated data required for training. In this work, we aim to accelerate this process using a computer-aided diagnosis of this severe disease with the help of transfer learning based on deep convolutional neural networks. We have suggested the Inception V-3 approach for image classification based on convolution neural networks. Our developed model has the potential to address this CNN model’s problem of classification accuracy, and with imaging data, our proposed method outperforms recent state-of-the-art approaches. The case study for digital forensics is an essential component of emerging technologies, and hence glaucoma detection plays a vital role in it.

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Section: Methodsmentioning

confidence: 99%

“…CNNs can acquire lower-level functionality from public databases using transfer learning, which compensates for data scarcity. Hatanaka et al [22] used a two-step DCNN to detect MAs while still filtering false positives. Natural language processing was used by Dai et al [23] to compensate for videos that were poorly monitored (NLP).…”

Section: Literature Reviewmentioning

confidence: 99%

Digital Forensics Use Case for Glaucoma Detection Using Transfer Learning Based on Deep Convolutional Neural Networks

Latif

Xiao

et al. 2021

Security and Communication Networks

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“…For the detection of diabetic retinopathy purpose, various machine learning techniques and methods were proposed, used and reported in the literature . Meanwhile, deep learning has been used in diabetic retinopathy detection in [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. However, the reported detection systems were concentrating on the diabetic retinopathy as general detection, and also on the diabetic retinopathy signs detection, using various machine learning methods, and deep learning among them.…”

Section: Diabetic Retinopathy Detectionmentioning

confidence: 99%

Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Deep Learning and Image Augmentation

Rahim

Palade

Almakky

et al. 2019

Lecture Notes in Computer Science

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Diabetic retinopathy is a significant complication of diabetes, produced by high blood sugar level, which causes damage to the retina. Effective diabetic retinopathy screening is required because diabetic retinopathy does not show any symptoms in the initial stages, and can cause blindness if it is not diagnosed and treated promptly. This paper presents a novel diabetic retinopathy automatic detection in retinal images by implementing efficient image processing and deep learning techniques. Besides diabetic retinopathy detection, the developed system integrates a novel detection of maculopathy into one detection system. Maculopathy is the damage to the macula, the eye part that is responsible for central vision. Therefore, the combined detection of diabetic retinopathy and maculopathy is essential for an effective screening of diabetic retinopathy. The paper investigates the capability of image pre-processing techniques based on data augmentation as well as deep learning for diabetic retinopathy and maculopathy detection. Computer-assisted clinical decision-making is inevitably transforming the diabetic retinopathy detection and management today, which is crucial for clinicians and patients alike. Therefore, a high degree of accuracy, with which computer algorithms can detect the diabetic retinopathy and maculopathy, is absolutely necessary.

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“…These features have been extracted by utilising many filters which exploit the natural structure of the data. Among all deep networks, CNN based ones have been most successful for DR grade classification [14], [15], [17], [19]. Noushin et al [14] proposed a two-step CNN for segmenting MAs in the input retinal scans.…”

Section: Introductionmentioning

confidence: 99%

“…Tan et al [16] employed CNN to detect exudates, MAs, and HEMs. For detecting MAs and filters false positives, Hatanaka et al [17] utilised a two-step DCNN. Gargeya and Leng [18] exploited CNN for extracting features from images, which were then fed to a tree-based model that classifies binary DR. Gulshan et al [20] used inception-v3 to detect DR grades.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Attention Network for Diabetic Retinopathy Classification

Al-Antary

Arafa

2021

IEEE Access

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Diabetic Retinopathy (DR) is a highly prevalent complication of diabetes mellitus, which causes lesions on the retina that affect vision which may lead to blindness if it is not detected and diagnosed early. Convolutional neural networks (CNN) are becoming the state-of-the-art approach for automatic detection of DR by using fundus images. The high-level features extracted by CNN are mostly utilised for the detection and classification of lesions on the retina. This high-level representation is capable of classifying different DR classes; however, more effective features for detecting the damages are needed. This paper proposes the multi-scale attention network (MSA-Net) for DR classification. The proposed approach applies the encoder network to embed the retina image in a high-level representational space, where the combination of mid and high-level features is used to enrich the representation. Then a multi-scale feature pyramid is included to describe the retinal structure in a different locality. Furthermore, to enhance the discriminative power of the feature representation a multi-scale attention mechanism is used on top of the high-level representation. The model is trained in a standard way using the cross-entropy loss to classify the DR severity level. In parallel as an auxiliary task, the model is trained using the weakly annotated data to detect healthy and non-healthy retina images. This surrogate task helps the model to enrich its discriminative power for distinguishing the non-healthy retina images. The proposed method when implemented has achieved outstanding results on two public datasets: EyePACS and APTOS.

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Automatic microaneurysms detection on retinal images using deep convolution neural network

Cited by 18 publications

References 7 publications

Digital Forensics Use Case for Glaucoma Detection Using Transfer Learning Based on Deep Convolutional Neural Networks

Digital Forensics Use Case for Glaucoma Detection Using Transfer Learning Based on Deep Convolutional Neural Networks

Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Deep Learning and Image Augmentation

Multi-Scale Attention Network for Diabetic Retinopathy Classification

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