Application of dynamical system theory in LC harmonic oscillator circuits: A complement tool to the Barkhausen criterion

Salas-Castro, Pablo; Delgado-Aranda, Finees; Tristán-Hernández, Edgar; Martínez-Montejano, Roberto C; Murguı́a, J. S.; Campos-Cantón, I.

doi:10.1177/0020720918770140

Cited by 2 publications

(1 citation statement)

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“…Salas-Castro et al have utilized the arteriolar-tovenular distance across proportion of retinal blood vessels as a pointer of disease related changes in the retinal blood vessel tree. 9 Their trial results show a 97.1% achievement rate in the recognizable proof of DR vessel beginning stages, and a 99.2% achievement rate in the following of retinal vessels. Another technique for surface-based vessel division to beat this issue was proposed.…”

Section: R E T R a C T E Dmentioning

confidence: 94%

RETRACTED: Detection of exudates using neuro-fuzzy technique

Jeyapriya

Umadevi

2020

International Journal of Electrical Engineering & Education

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Diabetic retinopathy is a common condition of an ailment where the retina is harmed on the grounds that fluid breaks away from the walls of blood vessels into the retina. The large population is affected by diabetic retinopathy and hence research in the area of automated screening has acquired major research interest in the scientific community. The diagnosing features for diabetic retinopathy comprises of identifying deformations of minute blood vessel in form of exudates and its features occurring in and around the regions of fundus which will result into exudes, hemorrhages, or microaneurysms. This feature is often hard to be classified by naked eyes and need trained personnel for close inspection before making diagnosis. Therefore, a comprehensive machine learning tool is in the demand for automated diabetic retinopathy detection. Neural networks have given promising results in the field of classification. Therefore, we present a hybrid neural network-based method with realistic clinical potential. In this study, we present a neuro-fuzzy technique, which is developed for identification of exudates in retinal images and is utilized for the extraction of essential features from the retinal images of patient’s eyeballs. We trained this hybrid neural network base technique over a set of 25,600 training images for color retinal images and tested it over 12,000 testing image set for binocular model. The method achieves an accuracy of 95.4% recognition on non-noisy images and 93% on noisy images; thereby giving a consistency rate of 91% in correspondence with the ophthalmologist.

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Section: R E T R a C T E Dmentioning

confidence: 94%