An efficient machine learning approach to nephrology through iris recognition
Tóm tắt
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.
Tài liệu tham khảo
Jiang J, Trundle P, Ren J. Medical image analysis with artificial neural networks. Compute Med Image Graph. 2010;34(8):617–31.
Zhang L, et al. Prevalence of chronic kidney disease in china: a cross-sectional survey. Lancet. 2012;379:815–22.
Noll M, Li X, Wesarg S. Automated kidney detection and segmentation in 3D ultrasound. In Proc Workshop Clin Image-Based Procedures 2014, pp. 83–90.
Cueto-Manzano AM, et al. Prevalence of chronic kidney disease in an adult population. Arch Med Res. 2014;45(6):507–13.
Singh A, et al. Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration. J Biomed Inform. 2015;53:220–8.
Zhang M, Wu T, Bennett KM. A novel Hessian based algorithm for rat kidney glomerulus detection in 3D MRI. In: Proc SPIE, Med Imag, Image Process Int Soc Opt Photon, Vol. 9413, Mar. 2015, Art. no. 94132N9.
Chen Z, et al. Diagnosis of patients with chronic kidney disease by using two fuzzy classifiers. Chemometr Intell Lab. 2016;153:140–5.
Polat H, Mehr HD, Cetin A. Diagnosis of chronic kidney disease based on support vector machine by feature selection methods. J Med Syst. 2017;41(4):55.
Akkasaligar PT, Biradar S, Diagnosis of renal calculus disease in medical ultrasound images. In Proc IEEE Int Conf Comput Intell Comput Res (ICCIC), Dec. 2017, pp. 1–5.
Subasi A, Alickovic E, Kevric J, Diagnosis of chronic kidney disease by using random forest. In Proc Int Conf Medical and Biological Engineering, Mar 2017, pp. 589–594.
Papademetriou V, et al. Chronic kidney disease, basal insulin glargine, and health outcomes in people with dysglycemia: the ORIGIN Study. Am J Med. 2017;130(12):1465.
Chen Z, Zhang Z, Zhu R, Xiang Y, Harrington PB. Diagnosis of patients with chronic kidney disease by using two fuzzy classifiers. Chemom Intell Lab Syst. 2016;153:140–5.
Muzamil S, Hussain T, Haider A, Waraich U, Ashiq U, Ayguade E. An intelligent iris based chronic kidney identification system. Symmetry. 2020;12(12):2066.
Hill NR, Fatoba ST, Oke JL, Hirst JA, O’Callaghan CA, Lasserson DS, Hobbs FR. Global prevalence of chronic kidney disease—a systematic review and meta-analysis. PloS ONE. 2016;11(7):e0158765.
Subas A, Alickovic E, Kevric J. Chronic kidney disease diagnosis using random forest.
Xie Y, Bowe B, Mokdad HA, Xian H, Yan Y, Li T, Maddukuri G, Tsai C, Floyd T, Al-Aly Z. Analysis of the global burden of disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int. 2018;94(3):567–81.
Shehata M, Khalifa F, Soliman A, Ghazal M, Taher F, El-Ghar MA, Dwyer AC, Gimel’farb G, Keynton RS, El-Baz A. Computeraided diagnostic system for early detection of acute renal transplant rejection using diffusion-weighted MRI. IEEE Trans Biomed Eng. 2019;66(2):539–52.
Weber C, Röschke L, Modersohn L, Lohr C, Kolditz T, Hahn U, Ammon D, Betz B, Kiehntopf M. Optimized identification of advanced chronic kidney disease and absence of kidney disease by combining different electronic health data resources and by applying machine learning strategies. J Clin Med. 2020;9(9):2955.
Husseina SE, Hassanb OA, Granat MH. Assessment of the potential iridology for diagnosing kidney disease using wavelet analysis and neural networks. Biomed Signal Process Control. 2013;8(6):534–41.
Simon A, Worthen DM, Mitas JA. An evaluation of iridology. J Am Med Assoc. 1979;242:1385–7.
Esteves RB, Moreroa JA, de Souza Pereiraa S, Mendes KD, Hegadorenc KM, Cardosoa L. Parameters to increase the quality of iridology studies: a scoping review. Eur J Integr Med. 2021.
A.D. Wibawa, M.A.R.R. Sitorus, M.H. Purnomo, Classification of iris image of patient chronic renal Failur (CRF) using watershed algorithm and support vector machine (SVM), J. Theor. Appl. Inf. Technol. 91 (2016) 390–396 http://www.jatit.org/volumes/Vol91No2/19Vol91No2.pdf. 2019. Accessed 30 July 2019.
Bansal A, Agarwal R, Sharma RK. Determining diabetes using iris recognition system. Int J Diabetes Dev Ctries. 2015;35:432–8. https://doi.org/10.1007/s13410-015-0296-1.
Rohan R, Bhagyalakshmi V, Rashmi HA, Domnick A, Divya CD, Gururaj HL. An efficient approach to nephrology through iris recognition. 2021.
https://www5.cs.fau.de/research/data/fundus-images/
DIARETDB0 and DIARETDB1
IStockphoto.com