An Intelligent Iris Based Chronic Kidney Identification System

Muzamil, Sohail; Hussain, Tassadaq; Haider, Amna; Waraich, Umber; Ashiq, Umair; Ayguadé, Eduard

doi:10.3390/sym12122066

Cited by 10 publications

(15 citation statements)

References 54 publications

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“…Detección de enfermedades mediante Zonas somatotópicas mediante la segmentación polar del Iris. (Muzamil et al 2020) Es importante destacar que la Iridología es una medicina alternativa; por lo que el presente sistema de pre diagnóstico pude auxiliar en la validación de este método de detección de enfermedades. (Far 2019) Iridología como herramienta semiológica en homeopatía.…”

Section: Nombre Artículo Resultadosunclassified

Práctica de la iridología como herramienta diagnóstica en medicina natural

2022

Ciencia Latina

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La Iridología es una seudociencia utilizada como herramienta diagnóstica que tiene a fin la prevención de posibles enfermedades futuras, así también es útil cómo método de recuperación del estado de salud del paciente; La Iridología va de la mano con la medicina natural u homeopatía, ya que, a través de la utilización de agentes naturales, se logra obtener resultados favorables sobre la salud de las personas. Objetivo: Analizar mediante una revisión bibliográfica la práctica de la Iridología como herramienta diagnóstica en medicina natural. Método: Se realizó una revisión de metaanálisis donde se ejecutó una búsqueda bibliográfica en: Google académico, SciELO, Medline, Dialnet, así como una búsqueda manual entre las referencias de los estudios seleccionados y en diferentes bibliotecas. Resultados: el artículo trata la teoría y la historia de la Iridología, las áreas que se identifican en el iris del ojo, así como las diferentes formas y coloraciones que se encuentran en el mismo. También se analizan algunos diagramas para poder llegar a un diagnóstico verídico. Conclusión: En Ecuador debemos realizar investigaciones sobre Iridología, porque no encontramos información científica sobre esto, sin embargo, la homeopatía es muy reconocida en dicho país.

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Section: Nombre Artículo Resultadosunclassified

Práctica de la iridología como herramienta diagnóstica en medicina natural

2022

Ciencia Latina

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“…In the linear regression model, an NRMSE between Y n and Ŷ n was found to be 0.1095 for Equation ( 14), 0.1954 for Equation (15), 0.1484 for Equation ( 16), and 0.2013 for Equation (17). While AGSV can be expressed using the total cholesterol level, HDL, LDL, and triglyceride levels as in Equation ( 14), Equation (15), Equation (16), and Equation (17), these cholesterol values can also be expressed using AGSV. These expressions and NRMSE values are given in Table 2.…”

Section: Resultsmentioning

confidence: 99%

“…12 Studies in the field of iridology have been undertaken for regions related to a particular tissue or organ on the iris pattern. Some of these studies are as follows: changes to the iris were examined to check the health of organs such as the kidney, [13][14][15][16] stomach, 17,18 lungs, 6,19 digestive and nervous systems, 12 heart, 20,21 and pancreas. [22][23][24][25][26] Some studies aimed to learn about the behavior and personality disorders of a given individual through iris analysis and a profile analysis performed on them.…”

Section: Introductionmentioning

confidence: 99%

An alternative approach for determining the cholesterol level: Iris analysis

Özbilgin

Kurnaz

2021

Int J Imaging Syst Tech

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In this study, the relationship between individuals' cholesterol levels and the width of the sodium ring was investigated. Firstly, iris images were taken of 100 individuals with total cholesterol levels of 200 mg/dL and above. The average grey scale value (AGSV) for each iris image with a sodium ring was calculated. Secondly, the relationship between the AGSV and cholesterol levels was investigated. Finally, a new mathematical model was proposed that described the relationship between AGSV and total cholesterol with ~89% accuracy. The relationship between AGSV and high-density lipoprotein (HDL), low-density lipoprotein (LDL), and triglyceride levels was calculated with an accuracy of 80.5%, 85.1%, and 79.9%, respectively. In addition, AGSV was determined with 90% accuracy using total cholesterol, HDL, LDL, and triglycerides using multiple linear regression. Although multiple linear regression gives better AGSV, the results have shown that multilayer perceptron (MLP) mapping method gives better accuracy up to 6.62%.

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“…Here they have used an iris image of a human as input and a deep neural network-based algorithm, in order to determine whether or not the person has a kidney problem. They have used a GPU based distributed computing machine for the testing and training of the algorithm [13].…”

Section: Literature Surveymentioning

confidence: 99%

An efficient machine learning approach to nephrology through iris recognition

Divya¹,

Gururaj²,

R³

et al. 2021

Discov Artif Intell

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Iridology is a technique in science used to analyze color, patterns, and various other properties of the iris to assess an individual's general health. Few regions in the iris are connected by nerves coming from different organs of body, this shows some special unique qualities which is advantageous along with which assist in psychological condition, particular organ conditions and construction of the body. The structural and designed patterns present on specific part of iris represent the level of intensity of disorder caused by the organs. This method of approach can be employed as reasonable and logical guidelines for the detection and identification of disorders. Therefore, after scanning the image of iris advance study of disorder can be carried out for detecting the condition of organ. Initially by the service of an adaptive histogram, the image of eye should be separated from part of the image captured. Next the images of iris are classified and recognized using machine learning algorithm Support Vector machine or Support Vector Networks. The features are extracted from images of iris using white Gaussian filters which are then used as a feature descriptor. These descriptors count the occurrences of gradient orientation and magnitude in localized portions of an image. Then convert the image of iris to a gray scaled image, final image is standardized. Next is to convert it into rectangular shape and then assembling the HMM images of eyes related to the kidney. The final level is to diagnose the edge of image of iris HMM. By analysing end results, condition of the organ can be diagnosed and results can be obtained from the iris recognition system.

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An Intelligent Iris Based Chronic Kidney Identification System

Cited by 10 publications

References 54 publications

Práctica de la iridología como herramienta diagnóstica en medicina natural

Práctica de la iridología como herramienta diagnóstica en medicina natural

An alternative approach for determining the cholesterol level: Iris analysis

An efficient machine learning approach to nephrology through iris recognition

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