RETRACTED ARTICLE: Deep learning assisted convolutional auto-encoders framework for glaucoma detection and anterior visual pathway recognition from retinal fundus images

Saravanan, Vijayalakshmi; Samuel, R. Dinesh Jackson; Krishnamoorthy, Sujatha; Manickam, Adhiyaman

doi:10.1007/s12652-021-02928-0

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…To validate the effectiveness of the proposed approach, the proposed AMF-RCNN method is used to identify eye diseases and is compared to several current detection methods based on CNNs [8, DL-CAEF [14], and ODALAs. The fundus pictures were arbitrarily divided into training (70%) and test (70%) (30%).…”

Section: Resultsmentioning

confidence: 99%

Identification of Retinal Disease using Anchor-Free Modified Faster Region

Arulselvam.¹,

Joseph²

2022

IJACSA

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Infections of the retinal tissue, as well as delayed or untreated therapy, may result in visual loss. Furthermore, when a large dataset is involved, the diagnosis is prone to inaccuracies. As a consequence, a completely automated model of retinal illness diagnosis is presented to eliminate human input while maintaining high accuracy classification findings. ODALAs (Optimal Deep Assimilation Learning Algorithms) are unable to handle zero errors or covariance or linearity and normalcy. DLTs (Deep Learning Techniques) such as GANs (Generative Adversarial Networks) or CNNs might replace the numerical solution of dynamic systems (Convolution Neural Networks), in order to speed up the runs. With this objective, this study proposes a completely automated multi-class retina disorders prediction system in which pictures from the Fundus image dataset are upgraded using RSWHEs (Recursive Separated Weighted Histogram Equalizations) to boost contrast and noise is eliminated using the Wiener filter. The improved picture is used for segmentation, which is done using clustering and the optimum threshold. The suggested EFFCM is used for clustering (Enriched Fast Fuzzy C Means). The suggested AOO (Adaptive optimum Otsu) threshold technique is used for clustering and picture optimal thresholding. This work suggests AMF-RCNNs (anchor-free modified faster region-based CNNs) that integrate AFRPNs (anchor free regions proposal generation networks) with Improved Fast R-CNNs into single networks for detecting retinal issues accurately. The performances of Accuracy is 98.5%, F-Measure is 96.5%, Precession is 99.2% and different Subset features are 98.5 % show better results when compared with other related techniques or models.

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Section: Resultsmentioning

confidence: 99%

Identification of Retinal Disease using Anchor-Free Modified Faster Region

Arulselvam.¹,

Joseph²

2022

IJACSA

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“…All these are computed using the vertical disc and cup heights. Automatic methods thus generally rely on analysis of the optic disc region for glaucoma diagnosis [16][17][18][19]. Three types of glaucoma detection algorithms are commonly presented in the literature: structural, generic, and hybrid.…”

Section: Iridocorneal Endothelial Syndromementioning

confidence: 99%

Creating an Early Diagnostic Method for Glaucoma Using Convolutional Neural Networks

Alqarni,

Al Harbi,

Subhan

2024

Preprint

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According to the World Health Organization, glaucoma is a leading cause of blindness, accounting for over 12% of global blindness as it affects one in every 100 people. In fact, 79.6 million people worldwide live with blindness caused by glaucoma. This is because the current method for diagnosing glaucoma is by examining retinal fundus images. However, it is considerably difficult to distinguish the lesions' features solely through manual observations by ophthalmologists, especially in the early phases. This study proposes a new diagnosis method using convolutional neural networks. The attention mechanism is utilized to learn pixel-wise features for accurate prediction. Several attention strategies have been developed to guide the networks in learning the important features and factors that affect localization accuracy. The algorithms were trained for glaucoma detection using Python 2.7, TensorFlow, Py Torch, and Keras. The methods were evaluated on Drishti-GS and RIM-ONE datasets with 361 training and 225 test sets, consisting of 344 healthy and 242 glaucomatous images. The proposed algorithms can achieve impressive results that show an increase in overall diagnostic efficiency, as the algorithm displays a 30-second detection time with 98.9% accuracy compared to the 72.3% accuracy of traditional testing methods. Finally, this algorithm has been implemented as a webpage, allowing patients to test for glaucoma. This webpage offers various services such as: connecting the patient to the nearest care setup; offering scientific articles regarding glaucoma; and a video game that supports eye-treatment yogic exercises to strengthen vision and focus. This early diagnostic method has the near future potential to decrease the percentage of irreversible vision loss due to glaucoma by 42.79% (the percentage was calculated using the mean absolute error function), which could prevent glaucoma from remaining the leading cause of blindness worldwide.

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“…Khamparia et al [41] used a Stack autoencoder for classifying chronic kidney disease data using Softmax as a classifier and utilizing multimedia data for the classification of chronic kidney from the UCI dataset, and its accuracy was 100%. Saravanan et al [42] proposed Deep Learning Assisted Convolutional Auto-Encoders Framework (DL-CAEF) is aimed at the early detection of glaucoma and recognition of the anterior visual pathway from retinal fundus images. The framework combines an encoder with a conventional CNN to minimize image reconstruction and classification errors.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature Enhanced Stacked Auto Encoder for Diseases Detection in Brain MRI

Muneer Butt,

Arif,

Letchmunan

et al. 2023

Computers, Materials &Amp; Continua

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The detection of brain disease is an essential issue in medical and research areas. Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging (MRI) images. These techniques involve training neural networks on large datasets of MRI images, allowing the networks to learn patterns and features indicative of different brain diseases. However, several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques. This paper implements a Feature Enhanced Stacked Auto Encoder (FESAE) model to detect brain diseases. The standard stack auto encoder's results are trivial and not robust enough to boost the system's accuracy. Therefore, the standard Stack Auto Encoder (SAE) is replaced with a Stacked Feature Enhanced Auto Encoder with a feature enhancement function to efficiently and effectively get non-trivial features with less activation energy from an image. The proposed model consists of four stages. First, pre-processing is performed to remove noise, and the greyscale image is converted to Red, Green, and Blue (RGB) to enhance feature details for discriminative feature extraction. Second, feature Extraction is performed to extract significant features for classification using Discrete Wavelet Transform (DWT) and Channelization. Third, classification is performed to classify MRI images into four major classes: Normal, Tumor, Brain Stroke, and Alzheimer's. Finally, the FESAE model outperforms the state-of-theart, machine learning, and deep learning methods such as Artificial Neural Network (ANN), SAE, Random Forest (RF), and Logistic Regression (LR) by achieving a high accuracy of 98.61% on a dataset of 2000 MRI images.The proposed model has significant potential for assisting radiologists in diagnosing brain diseases more accurately and improving patient outcomes.

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RETRACTED ARTICLE: Deep learning assisted convolutional auto-encoders framework for glaucoma detection and anterior visual pathway recognition from retinal fundus images

Cited by 11 publications

References 19 publications

Identification of Retinal Disease using Anchor-Free Modified Faster Region

Identification of Retinal Disease using Anchor-Free Modified Faster Region

Creating an Early Diagnostic Method for Glaucoma Using Convolutional Neural Networks

Feature Enhanced Stacked Auto Encoder for Diseases Detection in Brain MRI

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