Improving Supervised Microaneurysm Segmentation using Autoencoder-Regularized Neural Network

Kasantikul, Rangwan; Kusakunniran, Worapan

doi:10.1109/dicta.2018.8615839

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Residual connections are introduced in the encoding module, and large kernels for convolution are used at the lowest scale. Based on the autoencoder-regularized neural network model, Rangwan et al [57] proposed a novel MA segmentation technique. It is divided into two steps, the coarse-level stage firstly locates the candidate areas, then using the neural network to acquire confidence values of proposal areas of being MA in the fine-level segmentation stage.…”

Section: Methods For Clinical Features Detectionmentioning

confidence: 99%

Deep Learning and Its Application in Diabetic Retinopathy Screening

Zou

Zhu

et al. 2020

Chin. j. electron.

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Section: Methods For Clinical Features Detectionmentioning

confidence: 99%

Deep Learning and Its Application in Diabetic Retinopathy Screening

Zou

Zhu

et al. 2020

Chin. j. electron.

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“…For example, the method proposed by Ref. [3] segmented patches of microaneurysm in retinal images using the autoencoder-regularized neural network, while the feature-transfer network with local background suppression was proposed by Ref. [4] for microaneurysm detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting and staging diabetic retinopathy in retinal images using multi-branch CNN

Kusakunniran

Karnjanapreechakorn

Choopong

et al. 2022

ACI

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PurposeThis paper aims to propose a solution for detecting and grading diabetic retinopathy (DR) in retinal images using a convolutional neural network (CNN)-based approach. It could classify input retinal images into a normal class or an abnormal class, which would be further split into four stages of abnormalities automatically.Design/methodology/approachThe proposed solution is developed based on a newly proposed CNN architecture, namely, DeepRoot. It consists of one main branch, which is connected by two side branches. The main branch is responsible for the primary feature extractor of both high-level and low-level features of retinal images. Then, the side branches further extract more complex and detailed features from the features outputted from the main branch. They are designed to capture details of small traces of DR in retinal images, using modified zoom-in/zoom-out and attention layers.FindingsThe proposed method is trained, validated and tested on the Kaggle dataset. The regularization of the trained model is evaluated using unseen data samples, which were self-collected from a real scenario from a hospital. It achieves a promising performance with a sensitivity of 98.18% under the two classes scenario.Originality/valueThe new CNN-based architecture (i.e. DeepRoot) is introduced with the concept of a multi-branch network. It could assist in solving a problem of an unbalanced dataset, especially when there are common characteristics across different classes (i.e. four stages of DR). Different classes could be outputted at different depths of the network.

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Improving Supervised Microaneurysm Segmentation using Autoencoder-Regularized Neural Network

Cited by 2 publications

References 11 publications

Deep Learning and Its Application in Diabetic Retinopathy Screening

Deep Learning and Its Application in Diabetic Retinopathy Screening

Detecting and staging diabetic retinopathy in retinal images using multi-branch CNN

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