Retinal blood vessel segmentation from diabetic retinopathy images using tandem PCNN model and deep learning based SVM

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

doi:10.1016/j.ijleo.2019.163328

Cited by 69 publications

(38 citation statements)

References 27 publications

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“…TJ Jebaseeli et al. 9 proposed future practices which were appropriate for the prescreening outline of DR. This paper analyzed Contrast Limited Adaptive Histogram Equalization (CLAHE) model for removing the background from the original image and augments the foreground blood vessel pixels.…”

Section: Literature Surveysmentioning

confidence: 99%

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An effective technique for diabetic retinopathy using hybrid machine learning technique

Nimmagadda

Devi

2021

Stat Methods Med Res

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Diabetic retinopathy (DR) stays as an eye issue that has continuously developed in individuals who experienced diabetes. The complexities in diabetes cause harm to the vein at the back of the retina. In outrageous cases, DR could swift apparition disaster or visual impairment. This genuine impact had the option to charge through convenient treatment and early recognition. As of late, this issue has been spreading quickly, particularly in the working region, which in the end constrained the interest of an analysis of this disease from the most prompt stage. Therefore, that are castoff to protect the progressions of this disorder, revealing of the retinal blood vessels (RBVs) play a foremost role. The growth of an abnormal vessel leads to the development steps of DR, where it can be well known by extracting the RBV. The recognition of the BV for DR by developing an automatic approach is a major aim of our research study. In the proposed method, there are two major steps: one is segmentation and the second one is classification of affected retinal BV. The proposed method uses the Kinetic Gas Molecule Optimization based on centroid initialization used for the Fuzzy C-means Clustering. In the classification step, those segmented images are given as input to hybrid techniques such as a convolution neural network with bidirectional-long short-term memory (CNN with Bi-LSTM). The learning degree of Bi-LSTM is revised by using the self-attention mechanism for refining the classification accuracy. The trial consequences disclosed that the mixture algorithm achieved higher accuracy, specificity, and sensitivity than existing techniques.

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Section: Literature Surveysmentioning

confidence: 99%

“…The screening of DR, if infection is located in near Temecula, the infection is simple and primary stage. 9 In primary stage of DR scanning, the scan copies are dull or much blur condition. At the time segmentation process can most helpful for specialist to proper screening of affected part in eye.…”

Section: Introductionmentioning

confidence: 99%

An effective technique for diabetic retinopathy using hybrid machine learning technique

Nimmagadda

Devi

2021

Stat Methods Med Res

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“…Jebaseeli, Durai, and Peter (2019a) introduced a technique for segmentation of blood vessels. The quality of retinal images is improved by using CLAHE.…”

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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“…In 2017, Saleh et al proposed a DR assessment method using a fuzzy RF along with a dominancebased rough set balanced rule [25]. In 2019, Jebaseeli et al described a deep learning-based SVM (DLBSVM) for DR categorization [26]. They procured 201 fundus images from five different datasets and obtained near-perfect classification performance on them.…”

Section: Introductionmentioning

confidence: 99%

Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images

et al. 2021

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Diabetic Retinopathy (DR) refers to the damages endured by the retina as an effect of diabetes. DR has become a severe health concern worldwide, as the number of diabetes patients is soaring uncountably. Periodic eye examination allows doctors to detect DR in patients at an early stage to initiate proper treatments. Advancements in artificial intelligence and camera technology have allowed us to automate the diagnosis of DR, which can benefit millions of patients indeed. This paper inscribes a novel method for DR diagnosis based on the gray-level intensity and texture features extracted from fundus images using a decision tree-based ensemble learning technique. This study primarily works with the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection (APTOS 2019 BD) dataset. We undertook several steps to curate its contents to make them more suitable for machine learning applications. Our approach incorporates several image processing techniques, two feature extraction techniques, and one feature selection technique, which results in a classification accuracy of 94.20% (margin of error: ±0.32%) and an F-measure of 93.51% (margin of error: ±0.5%). Several other parameters regarding the proposed method’s performance have been presented to manifest its robustness and reliability. Details on each employed technique have been included to make the provided results reproducible. This method can be a valuable tool for mass retinal screening to detect DR, thus drastically reducing the rate of vision loss attributed to it.

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Retinal blood vessel segmentation from diabetic retinopathy images using tandem PCNN model and deep learning based SVM

Cited by 69 publications

References 27 publications

An effective technique for diabetic retinopathy using hybrid machine learning technique

An effective technique for diabetic retinopathy using hybrid machine learning technique

Classification of retinal images based on convolutional neural network

Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images

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