Retraction Note to: Detecting disorders in retinal images using machine learning techniques

Gnanaselvi, J. Anitha; Kalavathy, G. Maria

doi:10.1007/s12652-022-04248-3

Cited by 1 publication

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“…Medical specialists can use retinal fundus images to identify retinal diseases including DRs and retinitis pigmentosa. Studies using MLTs (Machine learning techniques) have been recently focusing on identification of retinal disorders including DRs by categorizing fundus images based on extracted features [5]. The main aspire of this paper is to differentiate automatically abnormalities in the retina without segmentations or feature extractions from their images and use DLTs to automatically categorize retinal images as healthy or sick.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%