Hardware Based Analysis on Automated Early Detection of Diabetic-Retinopathy

Datta, Niladri Sekhar; Banerjee, Rajib; Dutta, Himadri Sekhar; Mukhopadhyay, Sabyasachi

doi:10.1016/j.protcy.2012.05.039

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…The use of deep learning to diagnose diabetic retinopathy is also discussed [ 23 ]. Hardware-based methods have also been described in alternative method [ 24 ]. The study discusses the prospect of employing hardware to identify diabetic retinopathy utilizing a digital signal processor kit provided by Texas Instruments.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs

Gunasekaran

Pitchai²,

Chaitanya

et al. 2022

BioMed Research International

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Diabetic patients can also be identified immediately utilizing retinopathy photos, but it is a challenging task. The blood veins visible in fundus photographs are used in several disease diagnosis approaches. We sought to replicate the findings published in implementation and verification of a deep learning approach for diabetic retinopathy identification in retinal fundus pictures. To address this issue, the suggested investigative study uses recurrent neural networks (RNN) to retrieve characteristics from deep networks. As a result, using computational approaches to identify certain disorders automatically might be a fantastic solution. We developed and tested several iterations of a deep learning framework to forecast the progression of diabetic retinopathy in diabetic individuals who have undergone teleretinal diabetic retinopathy assessment in a basic healthcare environment. A collection of one-field or three-field colour fundus pictures served as the input for both iterations. Utilizing the proposed DRNN methodology, advanced identification of the diabetic state was performed utilizing HE detected in an eye’s blood vessel. This research demonstrates the difficulties in duplicating deep learning approach findings, as well as the necessity for more reproduction and replication research to verify deep learning techniques, particularly in the field of healthcare picture processing. This development investigates the utilization of several other Deep Neural Network Frameworks on photographs from the dataset after they have been treated to suitable image computation methods such as local average colour subtraction to assist in highlighting the germane characteristics from a fundoscopy, thus, also enhancing the identification and assessment procedure of diabetic retinopathy and serving as a skilled guidelines framework for practitioners all over the globe.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs

Gunasekaran

Pitchai²,

Chaitanya

et al. 2022

BioMed Research International

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“…The detection of diabetic retinopathy using deep learning is also presented in [14][15][16][17]. In another approach, hardware-based solutions have also been reported [18]. With a digital signal processing kit developed by Texas Instrument, the article reports the possibility of using hardware for detecting diabetic retinopathy.…”

Section: Introductionmentioning

confidence: 99%

Detecting Diabetic Retinopathy Using Embedded Computer Vision

Vora

Shrestha

2020

Applied Sciences

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Diabetic retinopathy is one of the leading causes of vision loss in the United States and other countries around the world. People who have diabetic retinopathy may not have symptoms until the condition becomes severe, which may eventually lead to vision loss. Thus, the medically underserved populations are at an increased risk of diabetic retinopathy-related blindness. In this paper, we present development efforts on an embedded vision algorithm that can classify healthy versus diabetic retinopathic images. Convolution neural network and a k-fold cross-validation process were used. We used 88,000 labeled high-resolution retina images obtained from the publicly available Kaggle/EyePacs database. The trained algorithm was able to detect diabetic retinopathy with up to 76% accuracy. Although the accuracy needs to be further improved, the presented results represent a significant step forward in the direction of detecting diabetic retinopathy using embedded computer vision. This technology has the potential of being able to detect diabetic retinopathy without having to see an eye specialist in remote and medically underserved locations, which can have significant implications in reducing diabetes-related vision losses.

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“…In [18] median filter is applied to the red channel of the retina image to reduce the noise in the retina, before the before a contrast-limited adaptive histogram equalization (CLAHE) process was applied for contrast enhancement. Earlier detection of DR is presented in [19]; here preprocessing, segmentation and classification are analyzed. To reduce the noise and preserve the edges sharply are maintained through median filter after the color space conversion and zero padding.…”

Section: Introductionmentioning

confidence: 99%

Restoration of retina images using extended median filter algorithm

Subbuthai

Muruganand

2015

2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)

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Diabetic Retinopathy is one of the complicated diseases and it is caused by the changes in the blood vessels of the retina. Extraction of retina image through Fundus camera sometimes produces poor contrast and noises; detection of blood vessels in the retina is very complicated. So preprocessing is needed, in this paper a novel algorithm is implemented to remove the noisy pixel in the retina image. The proposed algorithm is Extended Median Filter and it is applied to green channel of the retina, because green channel vessels are brighter than the background. Proposed extended median filter is compared with the existing standard median filter by performance metrics such as Peak Signal-to-Noise Ratio, Mean Square Error and Root Mean Square Error. Experimental results show that the proposed Extended Median Filter algorithm gives better result than the existing standard median filter in terms of noise suppression and detail preservation.

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Hardware Based Analysis on Automated Early Detection of Diabetic-Retinopathy

Cited by 7 publications

References 7 publications

A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs

A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs

Detecting Diabetic Retinopathy Using Embedded Computer Vision

Restoration of retina images using extended median filter algorithm

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