Detection of retinal abnormalities in fundus image using CNN deep learning networks

Akil, Mohamed; Elloumi, Yaroub; Kachouri, Rostom

doi:10.1016/b978-0-12-819740-0.00002-4

Cited by 20 publications

(11 citation statements)

References 93 publications

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“…The great challenge facing musical emotion recognition is that there is a difference between the characteristics of the audio signal and the emotional semantics of the music that is difficult to describe with physical parameters, and emotion recognition through deep learning may be able to cross this difference [ 17 ]. Convolutional neural network (CNN) [ 18 , 19 ] and RNN have demonstrated their excellent performance in many music classification tasks.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Emotion Recognition of Violin Playing Based on Big Data Analysis Technologies

Zou

2022

Journal of Environmental and Public Health

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The rapid development of Internet technology has promoted the vigorous development of the multimedia. As one of the most classic instruments, the violin has been fully developed in its creation, education, and performance. In the face of more and more violin performances, the effective organization and retrieval of these musical works is an urgent problem to be solved, while it is common to classify and organize music based on the emotional properties of the performance. Deep learning is a model based on feature hierarchy and unsupervised feature learning, which has strong learning ability and adaptability. Based on the recurrent neural network (RNN) method, long short-term memory (LSTM) is one of the classic models of deep learning that can effectively learn the characteristics of time series data and achieve effective predictions. Therefore, based on the classical Hevner emotion classification model, this paper proposes an emotion recognition method for dynamic violin performances based on LSTM, which selects acoustic features and classifies the audio acoustic signals contained in the violin performances. In order to verify the effectiveness of this method, this paper carries out data labeled, feature selection, and model test on the actual violin music data by turn. The results show that the proposed method can greatly reduce the training time and improve the prediction accuracy, which reached 83%, higher than the existing methods. Meanwhile, the accuracy and iteration times of violin playing music of different emotional categories are also counted. Moreover, the method is robust to the genre, timbre, and noise changes, and the emotion recognition effect is superior.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Emotion Recognition of Violin Playing Based on Big Data Analysis Technologies

Zou

2022

Journal of Environmental and Public Health

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“…Then, the results are summed to obtain an output value in the corresponding position (i,j) of the output feature map "O". This processing is computed as (1), where (Kpx, Kpy) denotes the coordinates of the kernel points.…”

Section: Convolution Processingmentioning

confidence: 99%

“…Several ocular pathologies are associated with abnormal variation of vessel anatomies. As in the proliferative stage of diabetic retinopathy [1], new tortuous vessels appear with lower thickness with respect to the predecessor ones. Further, in the nonproliferative diabetic retinopathy stages, numerous lesions such as microaneurysms and exudates appear close to the vessel tree [2,3].…”

Section: Introductionmentioning

confidence: 98%

“…Therefore, an automatic and an accurate vessel segmentation is required. With the development of deep learning and especially Convolutional Neural Networks (CNNs), various architecture are proposed and have been applied in various medical domains [1,4,5]. Certain of these architectures have been propounded for the segmentation tasks such as [6].…”

Section: Introductionmentioning

confidence: 99%

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Extended U-net for Retinal Vessel Segmentation

Boudegga

Elloumi

Kachouri

et al. 2022

Advances in Computational Collective Intelligence

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The retinal vascular tree is an important biomarker for the diagnosis of ocular dis-ease, where an efficient segmentation is highly required. Recently, various standard Convolutional Neural Networks CNN dedicated for segmentation are applied for retinal vessel segmentation. In fact, retinal blood vessels are presented in different retinal image resolutions with a complicated morphology. Thus, it is difficult for the standard configuration of CNN to guarantee an optimal feature extraction and efficient segmentation whatever the image resolution is. In this paper, new retinal vessel segmentation approach based on deep learning architecture is propounded. The idea consists of enlarging the kernel size of convolution layer in order to cover the vessel pixels as well as more neighbors for extracting features. Within this objective, our main contribution consists of identifying the kernel size in correlation with retinal image resolution through an experimental approach. Then, a novel U-net extension is proposed by using convolution layer with the identified kernel size. The suggested method is evaluated on two public databases DRIVE and HRF having different resolutions, where higher segmentation performances are achieved respectively with 5*5 and 7*7 convolution kernel sizes. The average accuracy and sensitivity values for DRIVE and HRF databases are respectively in the order of to 0.9785, 0.8474 and 0.964 and 0.803 which outperform the segmentation performance for the standard U-net.

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“…Deep learning in ocular imaging can be used in conjunction with telemedicine as a possible solution for selecting, diagnosing, and controlling ophthalmic diseases for patients in primary care [14]. Recent advances in neural network approaches are at the forefront of stateof-the-art disease recognition systems [15], [16]. Many researchers have made significant efforts to resolve the multi-label classification problem of ophthalmic disease.…”

Section: Related Workmentioning

confidence: 99%

Discriminative Kernel Convolution Network for Multi-Label Ophthalmic Disease Detection on Imbalanced Fundus Image Dataset

Bhati¹,

Gour²,

Khanna³

et al. 2022

Preprint

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It is feasible to recognize the presence and seriousness of eye disease by investigating the progressions in retinal biological structure. Fundus examination is a diagnostic procedure to examine the biological structure and anomaly of the eye. Ophthalmic diseases like glaucoma, diabetic retinopathy, and cataract are the main reason for visual impairment around the world. Ocular Disease Intelligent Recognition (ODIR-5K) is a benchmark structured fundus image dataset utilized by researchers for multi-label multi-disease classification of fundus images. This work presents a discriminative kernel convolution network (DKCNet), which explores discriminative region-wise features without adding extra computational cost. DKCNet is composed of an attention block followed by a squeeze and excitation (SE) block. The attention block takes features from the backbone network and generates discriminative feature attention maps. The SE block takes the discriminative feature maps and improves channel interdependencies. Better performance of DKCNet is observed with Inception-Resnet backbone network for multi-label classification of ODIR-5K fundus images with 96.08 AUC, 94.28 F1-score and 0.81 kappa score. The proposed method splits the common target label for an eye pair based on the diagnostic keyword. Based on these labels oversampling and undersampling is done to resolve class imbalance. To check the biasness of proposed model towards training data, the model trained on ODIR dataset is tested on three publicly available benchmark datasets. It is found to give good performance on completely unseen fundus images also.

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Detection of retinal abnormalities in fundus image using CNN deep learning networks

Cited by 20 publications

References 93 publications

[Retracted] Emotion Recognition of Violin Playing Based on Big Data Analysis Technologies

[Retracted] Emotion Recognition of Violin Playing Based on Big Data Analysis Technologies

Extended U-net for Retinal Vessel Segmentation

Discriminative Kernel Convolution Network for Multi-Label Ophthalmic Disease Detection on Imbalanced Fundus Image Dataset

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