Amini, N, Ameri, A. (1399). A Deep Learning Approach to Automatic Recognition of Arcus Senilis. سامانه مدیریت نشریات علمی, 10(4), 507-512. doi: 10.31661/jbpe.v0i0.2003-1080
N Amini; A Ameri. "A Deep Learning Approach to Automatic Recognition of Arcus Senilis". سامانه مدیریت نشریات علمی, 10, 4, 1399, 507-512. doi: 10.31661/jbpe.v0i0.2003-1080
Amini, N, Ameri, A. (1399). 'A Deep Learning Approach to Automatic Recognition of Arcus Senilis', سامانه مدیریت نشریات علمی, 10(4), pp. 507-512. doi: 10.31661/jbpe.v0i0.2003-1080
Amini, N, Ameri, A. A Deep Learning Approach to Automatic Recognition of Arcus Senilis. سامانه مدیریت نشریات علمی, 1399; 10(4): 507-512. doi: 10.31661/jbpe.v0i0.2003-1080
A Deep Learning Approach to Automatic Recognition of Arcus Senilis
1MSc, Department of Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2PhD, Department of Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
چکیده
Background: Arcus Senilis (AS) appears as a white, grey or blue ring or arc in front of the periphery of the iris, and is a symptom of abnormally high cholesterol in patients under 50 years old. Objective: This work proposes a deep learning approach to automatic recognition of AS in eye images. Material and Methods: In this analytical study, a dataset of 191 eye images (130 normal, 61 with AS) was employed where ¾ of the data were used for training the proposed model and ¼ of the data were used for test, using a 4-fold cross-validation. Due to the limited amount of training data, transfer learning was conducted with AlexNet as the pretrained network. Results: The proposed model achieved an accuracy of 100% in classifying the eye images into normal and AS categories. Conclusion: The excellent performance of the proposed model despite limited training set, demonstrate the efficacy of deep transfer learning in AS recognition in eye images. The proposed approach is preferred to previous methods for AS recognition, as it eliminates cumbersome segmentation and feature engineering processes.
Ramlee RA, Aziz KA, Ranjit S, Esro M. Automated detecting arcus senilis, symptom for cholesterol presence using iris recognition algorithm. Journal of Telecommunication, Electronic and Computer Engineering (JTEC). 2011;3(2):29-39.
Berggren L. Iridology: A critical reveiw. Acta Ophthalmologica. 1985;63(1):1-8. doi: 10.1111/j.1755-3768.1985.tb05205.x.
Morrison PJ. The iris–a window into the genetics of common and rare eye diseases. The Ulster medical journal. 2010;79(1):3-5. PubMed PMID: 20844723. PubMed PMCID: PMC2938985.
Anjarsari A, Damayanti A, Pratiwi AB, Winarko E. Hybrid radial basis function with firefly algorithm and simulated annealing for detection of high cholesterol through iris images. IOP Conf Ser: Mater Sci Eng; Malang, Indonesia: IOP Publishing Ltd; 2019. doi: 10.1088/1757-899X/546/5/052008.
Um JY, An NH, Yang GB, Lee GM, Cho JJ, Cho JW, Hwang WJ, et al. Novel approach of molecular genetic understanding of iridology: relationship between iris constitution and angiotensin converting enzyme gene polymorphism. The American journal of Chinese medicine. 2005;33(3):501-5. doi: 10.1142/S0192415X05003090. PubMed PMID: 16047566.
Songire SG, Joshi MS. Automated detection of cholesterol presence using iris recognition algorithm. International Journal of Computer Applications. 2016;133(6):41-5.
Simangunsong LP, Napitupulu IN, Lumbantoruan RE, et al. The Expert System of Cholesterol Detection Based on Iris Using the Gabor Filter. SinkrOn. 2019;4(1):13-8. doi: 10.33395/sinkron.v4i1.10161.
Goodfellow I, Bengio Y, Courville A. Deep learning. MIT press; 2016.
Ameri A. EMG-based wrist gesture recognition using a convolutional neural network. Tehran Univ Med J. 2019;77(7):434-9.
Ameri A, Akhaee MA, Scheme E, Englehart K. Regression convolutional neural network for improved simultaneous EMG control. Journal of neural engineering. 2019;16(3):036015.
Shridhar K, Laumann F, Liwicki M. A comprehensive guide to bayesian convolutional neural network with variational inference. arXiv: 2019.
Alom MZ, Taha TM, Yakopcic C, et al. A state-of-the-art survey on deep learning theory and architectures. Electronics. 2019;8(3):292. doi: 10.3390/electronics8030292.
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. 25th International Conference on Neural Information Processing Systems; United States: NIPS; 2012. p. 1097–105.
George A, Routray A. Real-time eye gaze direction classification using convolutional neural network. 2016 International Conference on Signal Processing and Communications (SPCOM); Bangalore, India: IEEE; 2016. p. 1-5. doi: 10.1109/SPCOM.2016.7746701.
Khan S, Rahmani H, Shah SA, Bennamoun M. A guide to convolutional neural networks for computer vision. Morgan & Claypool; 2018. doi: 10.2200/S00822ED1V01Y201712COV015.
