Diabetic retinopathy detection through convolutional neural networks with synaptic metaplasticity

Vives-Boix, Víctor; Ruiz-Fernández, Daniel

doi:10.1016/j.cmpb.2021.106094

Cited by 44 publications

(18 citation statements)

References 36 publications

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“…VíctorVives-Boix, and Daniel Ruiz-Fernández, 2021, Metaplasticity is intended to be implemented in convolutional neural networks i.e. metaplasticity in the backpropagation stage of CNN is used to diagnose DR in this work [ 184 ]. The dataset is chosen from Kaggle for the implementation of this work and it has been observed that Inception V3 with metaplasticity achieves better result scoring an accuracy, precision, recall, and F1 score of 95.56%, 98.9%, 90%, and, 94.24% respectively.…”

Section: Dr Screening Methodsmentioning

confidence: 99%

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Dubey

Dixit

2022

Multimed Tools Appl

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Diabetes is a long-term condition in which the pancreas quits producing insulin or the body’s insulin isn’t utilised properly. One of the signs of diabetes is Diabetic Retinopathy. Diabetic retinopathy is the most prevalent type of diabetes, if remains unaddressed, diabetic retinopathy can affect all diabetics and become very serious, raising the chances of blindness. It is a chronic systemic condition that affects up to 80% of patients for more than ten years. Many researchers believe that if diabetes individuals are diagnosed early enough, they can be rescued from the condition in 90% of cases. Diabetes damages the capillaries, which are microscopic blood vessels in the retina. On images, blood vessel damage is usually noticeable. Therefore, in this study, several traditional, as well as deep learning-based approaches, are reviewed for the classification and detection of this particular diabetic-based eye disease known as diabetic retinopathy, and also the advantage of one approach over the other is also described. Along with the approaches, the dataset and the evaluation metrics useful for DR detection and classification are also discussed. The main finding of this study is to aware researchers about the different challenges occurs while detecting diabetic retinopathy using computer vision, deep learning techniques. Therefore, a purpose of this review paper is to sum up all the major aspects while detecting DR like lesion identification, classification and segmentation, security attacks on the deep learning models, proper categorization of datasets and evaluation metrics. As deep learning models are quite expensive and more prone to security attacks thus, in future it is advisable to develop a refined, reliable and robust model which overcomes all these aspects which are commonly found while designing deep learning models.

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Section: Dr Screening Methodsmentioning

confidence: 99%

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Dubey

Dixit

2022

Multimed Tools Appl

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“…The authors achieved a binary classification accuracy of 97% and multiclass classification accuracy of 82%. Another study [ 30 ] introduces artificial synaptic meta plasticity into the initial learning stages of different CNNs for enhancing the feature extraction by the CNN models. They achieved an average accuracy of 94% on binary classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Diabetic Retinopathy Detection from Fundus Images of the Eye Using Hybrid Deep Learning Features

et al. 2022

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Diabetic Retinopathy (DR) is a medical condition present in patients suffering from long-term diabetes. If a diagnosis is not carried out at an early stage, it can lead to vision impairment. High blood sugar in diabetic patients is the main source of DR. This affects the blood vessels within the retina. Manual detection of DR is a difficult task since it can affect the retina, causing structural changes such as Microaneurysms (MAs), Exudates (EXs), Hemorrhages (HMs), and extra blood vessel growth. In this work, a hybrid technique for the detection and classification of Diabetic Retinopathy in fundus images of the eye is proposed. Transfer learning (TL) is used on pre-trained Convolutional Neural Network (CNN) models to extract features that are combined to generate a hybrid feature vector. This feature vector is passed on to various classifiers for binary and multiclass classification of fundus images. System performance is measured using various metrics and results are compared with recent approaches for DR detection. The proposed method provides significant performance improvement in DR detection for fundus images. For binary classification, the proposed modified method achieved the highest accuracy of 97.8% and 89.29% for multiclass classification.

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“…But the scheme failed to implement in huge datasets. In a convolutional neural network (CNN), with the use of fundus colored image Vives-Boxis et al 19 proposed a bio-inspired approach to synaptic meta plasticity for Abdelsalam et al 20 presented an approach for automatic early detection of DR by artificial neural networks (ANN) with the use of optical coherence tomography angiography (OCTA) images. The major objective of this research was to attain effective and robust automatic DR detection.…”

Section: Related Workmentioning

confidence: 99%

“…But the scheme failed to implement in huge datasets. In a convolutional neural network (CNN), with the use of fundus colored image Vives‐Boxis et al 19 proposed a bio‐inspired approach to synaptic meta plasticity for diabetic retinopathy detection. For each convolutional layer, synaptic meta plasticity is included in the backpropagation step of a convolutional process.…”

Section: Related Workmentioning

confidence: 99%

An efficient early detection of diabetic retinopathy using dwarf mongoose optimization based deep belief network

Abirami

Kavitha

2022

Concurrency and Computation

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In general, diabetic retinopathy (DR) is a common ocular disease that causes damage to the retina due to blood leakage from the vessels. Earlier detection of DR becomes a complicated task and it is necessary to prevent complete blindness. Various physical examinations are employed in DR detection but manual diagnosis results in misclassification results. Therefore, this article proposes a novel technique to predict and classify the DR disease effectively. The significant objective of the proposed approach involves the effective classification of fundus retinal images into two namely, normal (absence of DR) and abnormal (presence of DR). The proposed DR detection utilizes three vital phases namely, the data preprocessing, image augmentation, feature extraction, and classification. Initially, the image preprocessing is done to remove unwanted noises and to enhance images. Then, the preprocessed image is augmented to enhance the size and quality of the training images. This article proposes a novel modified Gaussian convolutional deep belief network based dwarf mongoose optimization algorithm for effective extraction and classification of retinal images. In this article, an ODIR-2019 dataset is employed in detecting and classifying DR disease.Finally, the experimentation is carried out and the proposed approach achieved 97% of accuracy. This implies that our proposed approach effectively classifies the fundus retinal images.

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Diabetic retinopathy detection through convolutional neural networks with synaptic metaplasticity

Cited by 44 publications

References 36 publications

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Diabetic Retinopathy Detection from Fundus Images of the Eye Using Hybrid Deep Learning Features

An efficient early detection of diabetic retinopathy using dwarf mongoose optimization based deep belief network

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