Diabetic retinopathy severity grading employing quadrant‐based <scp>Inception‐V3</scp> convolution neural network architecture

Bhardwaj, Charu; Jain, Shruti; Sood, Meenakshi

doi:10.1002/ima.22510

Cited by 28 publications

(9 citation statements)

References 49 publications

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“…Next, the proposed DL-KS segmentation technique is combined with several CNN architectures including Alex-Net [29], Inception-v3 [30], ResNet-50 [29], VGG-16 [31] and VGG-19 [32]. Figure 16 ree other datasets, such as Kaggle [33], Messidor [34], and DDR [35], are also used as the testbed for analyzing the performance of the proposed DS-KL segmentation technique.…”

Section: Implementation and Discussionmentioning

confidence: 99%

Pixel-Boundary-Dependent Segmentation Method for Early Detection of Diabetic Retinopathy

Sandhya

Suhasini

2022

Mathematical Problems in Engineering

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Early and precise detection of diabetic retinopathy prevents vision impairments through computer-aided clinical procedures. Identifying the symptoms and processing those by using sophisticated clinical procedures reduces hemorrhage kind of risks. The input diabetic retinopathy images are influenced by using computer vision-based processes for segmentation and classification through feature extractions. In this article, a delimiting segmentation using knowledge learning (DS-KL) is introduced for classifying and detecting exudate regions by using varying histograms. The input image is identified for its histogram changes from the feature-dependent segmentation process. Depending on the training knowledge from multiple inputs with different exudate regions, the segmentation is performed. This segmentation identifies infected and noninfected regions across the delimiting pixel boundaries. The knowledge-learning process stores the newly identified exudate region for training and pixel correlation. The recurrent training improves the segmentation accuracy with precise detection and limited errors.

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Section: Implementation and Discussionmentioning

confidence: 99%

Pixel-Boundary-Dependent Segmentation Method for Early Detection of Diabetic Retinopathy

Sandhya

Suhasini

2022

Mathematical Problems in Engineering

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“…At this stage, the process of augmentation of data on diabetic retinopathy disease images aims to manipulate the number of images by providing several image augmentation techniques so that the image is recognized as a different image but still maintains the core of the image [20]. Some types of augmentation processes are implemented in preprocessing datasets.…”

Section: Augmentationmentioning

confidence: 99%

Classification of Diabetic Retinopathy Disease Using Convolutional Neural Network

Minarno

Mandiri

Azhar

et al. 2022

JOIV : Int. J. Inform. Visualization

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Diabetic Retinopathy (DR) is a disease that causes visual impairment and blindness in patients with it. Diabetic Retinopathy disease appears characterized by a condition of swelling and leakage in the blood vessels located at the back of the retina of the eye. Early detection through the retinal fundus image of the eye could take time and requires an experienced ophthalmologist. This study proposed a deep learning method, the Efficientnet-b7 model to identify diabetic retinopathy disease automatically. This study applies three preprocessing techniques that could be implemented in the dataset "APTOS 2019 Blindness Detection". In preprocessing technique trial scenarios, Usuyama preprocessing technique obtained the best results with accuracy of 89% of train data and 84% in test data compared to Harikrishnan preprocessing technique which has 82% accuracy in test data, and Ben Graham preprocessing has 81% accuracy in test data. In this study, Hyperparameter tuning was conducted to find the best parameters for use on the EfficientNet-B7 Model. In this study, we tested the Efficientnet-B7 model with an augmentation process that can reduce the occurrence of overfitting compared to models without augmentation. Preprocessing techniques and augmentation techniques can influence the proposed EfficientNet-B7 model in terms of performance results and reduce the overfitting of models.

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“…Another transferable deep CNN model was proposed by Yosinski, et al [28] along with specialized different layers which transfers the features from the distant tasks outperforming the weight randomization. GoogleNet Inception-V3 platform was utilized by Esteva, et al [29] for the classification of skin lesions. This network was initially pre-trained over ImageNet dataset and then transferred to a clinical image platform using transfer learning.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis on transformer vibration signal recognition based on convolutional neural network

Hou¹,

Cai²

2021

J. vibroeng.

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In order to study the relationship between the transformer vibration and the operation state, the wavelet analysis method and the convolutional neural network method were used to analyze the transformer vibration signal. This paper proposes a transformer based on convolution neural network-based surface vibration signal feature extraction method. The result show that the convolution of neural network in different station transformer surface vibration signal classification has a lot of advantage, as the integration of feature extraction and classification recognition process together can effectively classify vibration signal recognition processing. This method is feasible for classification and identification by providing an accuracy value of 92.74 %. The future perspective of this research will focus on a generalized network model and parameters through experimentation for further investigation of accuracy and efficiency of this method.

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Diabetic retinopathy severity grading employing quadrant‐based Inception‐V3 convolution neural network architecture

Cited by 28 publications

References 49 publications

Pixel-Boundary-Dependent Segmentation Method for Early Detection of Diabetic Retinopathy

Pixel-Boundary-Dependent Segmentation Method for Early Detection of Diabetic Retinopathy

Classification of Diabetic Retinopathy Disease Using Convolutional Neural Network

Analysis on transformer vibration signal recognition based on convolutional neural network

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