IOT based sustainable diabetic retinopathy diagnosis system

Jebaseeli, T. Jemima; Durai, C. Anand Deva; Peter, J. Dinesh

doi:10.1016/j.suscom.2018.08.004

Cited by 15 publications

(10 citation statements)

References 28 publications

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“…Accuracy = = 0.7609 (4) Although the accuracy of the method proposed in [16] is 99.58%, its sensitivity is low, i.e. 72.51%.…”

Section: Resultsmentioning

confidence: 99%

“…They first detected hemorrhages and micro aneurisms (HMA) from fundus images and extracted some features related to color, shape, texture and gradient. An IOT based diagnosis of diabetic retinopathy (DR) was studied in [16] and they determined the blood glucose level of diabetics via Dexcom G4Plantinum sensors. Readings of the sensor were send to DRapp device and the patient's diabetes type was determined as type 1 or type 2.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Networks Based Decision Support System for the Detection of Diabetic Retinopathy

Şentürk

2020

Sakarya University Journal of Science

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Machine learning methods have been frequently used for the diagnosis of several diseases recently because of its reliability and convenience. In this paper, a comprehensive overview of the literature related to diabetes and diabetic retinopathy has been done and diagnosis of diabetic retinopathy disease is investigated. Artificial Neural Networks (ANN) method has been applied to the problem using Rapid Miner, a data mining tool. Some other methods have also adapted to the problem, but ANN based detection approach gave the best results. 88.52% sensitivity has been obtained using the features of Messidor dataset. Besides showing the success of ANN in diabetic retinopathy detection, this study also proved that Rapid Miner can be used effectively for the analysis of diabetic retinopathy.

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“…Accuracy = = 0.7609 (4) Although the accuracy of the method proposed in [16] is 99.58%, its sensitivity is low, i.e. 72.51%.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks Based Decision Support System for the Detection of Diabetic Retinopathy

Şentürk

2020

Sakarya University Journal of Science

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“…For type 2 diabetes, the iphone device takes eye images of the patient, and the DRapp is used for the diagnosis of the disease. The IoT consists of various layers such as data link layer, network layer and application layer (Jebaseeli, Durai, and Peter (2018).…”

Section: Related Workmentioning

confidence: 99%

Classification of retinal images based on convolutional neural network

El-Hag

Sedik

El‐Shafai

et al. 2020

Microscopy Res & Technique

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Automatic detection of maculopathy disease is a very important step to achieve high‐accuracy results for the early discovery of the disease to help ophthalmologists to treat patients. Manual detection of diabetic maculopathy needs much effort and time from ophthalmologists. Detection of exudates from retinal images is applied for the maculopathy disease diagnosis. The first proposed framework in this paper for retinal image classification begins with fuzzy preprocessing in order to improve the original image to enhance the contrast between the objects and the background. After that, image segmentation is performed through binarization of the image to extract both blood vessels and the optic disc and then remove them from the original image. A gradient process is performed on the retinal image after this removal process for discrimination between normal and abnormal cases. Histogram of the gradients is estimated, and consequently the cumulative histogram of gradients is obtained and compared with a threshold cumulative histogram at certain bins. To determine the threshold cumulative histogram, cumulative histograms of images with exudates and images without exudates are obtained and averaged for each type, and the threshold cumulative histogram is set as the average of both cumulative histograms. Certain histogram bins are selected and thresholded according to the estimated threshold cumulative histogram, and the results are used for retinal image classification. In the second framework in this paper, a Convolutional Neural Network (CNN) is utilized to classify normal and abnormal cases.

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“…IoT, using futuristic algorithms and protocol, developed the largest contributors to worldwide communications. It connects a huge amount of wireless sensors, devices, electronic devices, and home appliances to the Internet [8,9].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Chaotic Butterfly Optimization with Deep Neural Network based Sustainable Healthcare Management Systems

Abualkishik¹,

Alwan²

2021

AJBOR

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Sustainable healthcare systems are developed to priorities healthcare services involving difficult decision-making processes. Besides, wearables, internet of things (IoT), and cloud computing (CC) concepts are involved in the design of sustainable healthcare systems. In this study, a new Multi-objective Chaotic Butterfly Optimization with Deep Neural Network (MOCBOA-DNN) is presented for sustainable healthcare management systems. The goal of the MOCBOA-DNN technique aims to cluster the healthcare IoT devices and diagnose the disease using the collected healthcare data. The MOCBOA technique is derived to perform clustering process and also to tune the hyperparameters of the DNN model. Primarily, the clustering of IoT healthcare devices takes place using a fitness function to select an optimal set of cluster heads (CHs) and organize clusters. Followed by, the collected healthcare data are sent to the cloud server for further processing. Furthermore, the DNN model is used to investigate the healthcare data and thereby determine the presence of disease or not. In order to ensure the betterment of the MOCBOA-DNN technique, an extensive simulation analysis take place. The experimental results portrayed the supremacy of the MOCBOA-DNN technique over the other existing techniques interms of diverse evaluation parameters.

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IOT based sustainable diabetic retinopathy diagnosis system

Cited by 15 publications

References 28 publications

Artificial Neural Networks Based Decision Support System for the Detection of Diabetic Retinopathy

Artificial Neural Networks Based Decision Support System for the Detection of Diabetic Retinopathy

Classification of retinal images based on convolutional neural network

Multi-objective Chaotic Butterfly Optimization with Deep Neural Network based Sustainable Healthcare Management Systems

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