An automatic retinal vessel segmentation approach based on Convolutional Neural Networks

Mohamed, Chala; Nsiri, Benayad; Alaoui, My Hachem El Yousfi; Soulaymani, Abdelmajid; Benaji, Brahim

doi:10.1016/j.eswa.2021.115459

Cited by 28 publications

(10 citation statements)

References 26 publications

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“…Retinal blood vessel segmentation using a multi-encoder decoder architecture having two encoders was performed by the researchers in [27]. Yadav [28] used a dual-tree discrete Ridgelet transform (DT-DRT) to extract features within the Region of Interest in fundus images.…”

Section: Diabetic Retinopathy Retinal Vasculature Segmentationmentioning

confidence: 99%

GAN-Based Approach for Diabetic Retinopathy Retinal Vasculature Segmentation

Sebastian,

Elharrouss,

Al-Maadeed

et al. 2023

Bioengineering

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Most diabetes patients develop a condition known as diabetic retinopathy after having diabetes for a prolonged period. Due to this ailment, damaged blood vessels may occur behind the retina, which can even progress to a stage of losing vision. Hence, doctors advise diabetes patients to screen their retinas regularly. Examining the fundus for this requires a long time and there are few ophthalmologists available to check the ever-increasing number of diabetes patients. To address this issue, several computer-aided automated systems are being developed with the help of many techniques like deep learning. Extracting the retinal vasculature is a significant step that aids in developing such systems. This paper presents a GAN-based model to perform retinal vasculature segmentation. The model achieves good results on the ARIA, DRIVE, and HRF datasets.

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Section: Diabetic Retinopathy Retinal Vasculature Segmentationmentioning

confidence: 99%

GAN-Based Approach for Diabetic Retinopathy Retinal Vasculature Segmentation

Sebastian,

Elharrouss,

Al-Maadeed

et al. 2023

Bioengineering

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“…A convolutional neural network is one of the fields in deep learning methods [25]; it is widely used in computer vision. The good point of this deep learning algorithm is the possibility to predict and classify the data without a required pre-processing of the data like machine learning, but it can learn from large amounts of data [20].…”

Section: Definition Of Convolutional Neural Networkmentioning

confidence: 99%

Identifying Retinal Diseases on OCT Image Based on Deep Learning

Errabih¹,

Boussarhane²,

Nsiri

et al. 2022

Int. J. Onl. Eng.

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Computer-aided diagnosis has the potential to replace or at least support medical personnel in their everyday responsibilities such as diagnosis, therapy, and surgery. In the area of ophthalmology, artificial intelligence approaches have been incorporated in the diagnosis of the most frequent ocular disorders, such as choroidal neovascularization (CNV), diabetic macular oedema (DMO), and DRUSEN; these illnesses pose a significant risk of vision loss. Optical coherence tomography (OCT) is an imaging technology used to diagnose the aforementioned eye disorders. It enables ophthalmologists to see the back of the eye and take various slices of the retina. The goal of this research is to automate the diagnosis of retinopathy, which includes CNV, DME, and DRUSEN. The approach employed is a deep learning-based, and transfer learning technique, applying to a public dataset of OCT pictures and two pertained neural network models VGG16 and InceptionV3, which are trained on the big database "ImageNet." That allows them to be able to extract the main features of millions of images. Furthermore, fine-tuning approaches are applied to outperform the feature extraction method, by modifying the hyperparameters. The findings showed that the VGG16 model performed better in classification than the InceptionV3 architecture, with a 0.93 accuracy.

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“…In the dataset augmentation stage, the waveletbased shrinkage filtering method was implemented [56], in which two mother wavelets were included for filtering the signals: Daubechies 4 (db4) and Daubechies 6 (db6); for each signal, two new signals were generated. Data augmentation is carried out to improve performance and reduce overfitting in machine/deep learning algorithms [57]. Following signal augmentation, the ECG signals were cut to a duration of 10 seconds to maintain the order of presentation of the stimuli.…”

Section: B Data Processingmentioning

confidence: 99%

Heart Response to Harmonic Music Interval Stimuli Via Deep Learning Structures

Idrobo-Ávila¹,

Correa²,

Muñoz-Bolaños³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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The effect of music on the heart is reflected in variables such as heart rate and electrocardiographic (ECG) signals. ECG is a record of heart electrical activity and is a useful tool in diagnosing various cardiopathies. Artificial intelligence techniques have recently been implemented to analyze ECG and RR-interval data and are used thus in the present study to examine the influence on the heart of harmonic musical intervals and colored noise. Harmonic intervals were chosen because of their emotional response, while noise has been linked to positive responses such as improved sleep quality. A deep learning system was implemented, employing the ResNet-18 and GoogLeNet pre-trained networks to discriminate 31 different classes of ECG and RR-interval responses to the sound stimuli. Following an exploratory approach, deep learning was selected as an alternative to traditional analysis with the expectation that it could be incorporated into future music perception research. Classification revealed the ability of the implemented system to demonstrate heart response to the stimuli. ECG signals performed best, with 97% accuracy and Matthew's coefficient of 0.97, while RR-interval achieved a 93% accuracy and Matthews coefficient of 0.93, suggesting that the considered stimuli of harmonic musical intervals and noise produced different responses in the heart. Moreover, the Matthews coefficient values above 0.7 and close to 1 imply a correlation between the two types of stimuli and the heart response, as measured by ECG and RR-interval signals.

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An automatic retinal vessel segmentation approach based on Convolutional Neural Networks

Cited by 28 publications

References 26 publications

GAN-Based Approach for Diabetic Retinopathy Retinal Vasculature Segmentation

GAN-Based Approach for Diabetic Retinopathy Retinal Vasculature Segmentation

Identifying Retinal Diseases on OCT Image Based on Deep Learning

Heart Response to Harmonic Music Interval Stimuli Via Deep Learning Structures

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