Abstract
Traditional IT with advent of convolution neural networks and deep learning can lead a trendsetter for healthcare sector, diseases identification and prediction. An essential upsurge during pandemic is virtualization of hospital functional policies and care models. Virtual models are providing solutions for disease detection without consulting a doctor in turn help to enhance patient care and performance. These architectural and operational policies are significant for healthcare domain to enable strategic decision-making for intricate and sensitive environment. The machine learning models will reveal information from the historical, optimize the present and even predict the future performance of the different areas analysed. This paper reveals the challenges of healthcare with automated Cataract Detection and Grading System. The proposed system uses an efficient deep learning model with CNN and LSTM for detecting and classifying healthy eye from cataract eye. The proposed system produced an accuracy of 98.5 for the custom dataset.
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References
American Optometric Association (2006) School-aged vision: 6 to 18 years of age [homepage on the Internet]. http://www.aoa.org/patients-and-public/good-vision-throughout-life/childrens-vision/school-aged-vision-6-to-18-years-of-age?sso=y. Accessed 7 Jan 2017
Brenner MH, Curbow B, Javitt JC, Legro MW, Sommer A (1993) Vision change and quality of life in the elderly Response to cataract surgery and treatment of other chronic ocular conditions. Arch Ophthalmol 111(5):680–685
Dahyot R (2009) Statistical Hough Transform. IEEE Trans Pattern Anal Mach Intell 31(8):1502–1509
Dai L, Fanase R, Li H, Hou X, Sheng B, Wu Q, Jia W (2018) Clinical report guided retinal microaneurysm detection with multi-sieving deep learning. IEEE Trans Med Imaging 37:1149–1161. https://doi.org/10.1109/TMI.2018.2794988
Feng S, Zhuo Z, Pan D, Tian Q (2020) Ccnet: A cross-connected convolutional network for segmenting retinal vessels using multi-scale features. Neurocomputing 392:268–276. https://doi.org/10.1016/j.neucom.2018.10.098
Flaxman SR, Bourne RRA, Resnikoff S, Ackland P, Barithwaite T (2017) Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis. Lancet Glob Health 5:1221–1234
Guo L, Yang JJ, Peng L, Li J, Liang Q (2015) A computer aided healthcare system for cataract classification and grading based on fundus image analysis. Elsevier Science Publishers B. V, Amsterdam
Li H, Lim JH, Liu J, Mitchell P, Tan AG, Wang JJ, Wong TY (2010) A computer-aided diagnosis system of nuclear cataract. IEEE Trans Biomed Eng 57(7):1690–1698. https://doi.org/10.1109/TBME.2010.2041454
Mahdianpari M, Salehi B, Rezaee M, Mohammadimanesh F, Zhang Y (2018) Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery. Remote Sens 10(7):1119. https://doi.org/10.3390/rs10071119
McKean-Cowdin R, Varma R, Wu J, Hays RD, Azen SP, Los Angeles Latino Eye Study G (2007) Severity of visual field loss and health-related quality of life. Am J Ophthalmol 143(6):1013–1023 (PMID: 17399676)
Park HS, Kim HS, Jung HJ, Cho H (2014) Development of m-health application based on medical informatics standards. J Korea Multimedia Soc 17:640–653. https://doi.org/10.9717/kmms.2014.17.5.640
Qiao Z, Zhang Q, Dong Y, Yang J (2017) Application of SVM based on genetic algorithm in classification of cataract fundus images. IEEE Int Confer Imaging Syst Tech (IST) 2017:1–5. https://doi.org/10.1109/IST.2017.826154
Sachdeva P, Singh KJ (2015) Automatic segmentation and area calculation of optic disc in ophthalmic images. In: 2015 2nd international conference on recent advances in engineering and computational sciences (RAECS)
Sertkaya ME, Ergen B, Togacar M (2019) Diagnosis of eye retinal diseases based on convolutional neural networks using optical coherence images. In: 2019 23rd international conference electronics, pp 1–5
Shahriari HA, Izadi S, Rouhani MR, Ghasemzadeh F, Maleki AR (2007) Prevalence and causes of visual impairment and blindness in Sistan-va-Baluchestan Province, Iran: Zahedan Eye Study. Br J Ophthalmol 91(5):579–584 (PMID: 17124245)
Tariq A, Shaheen I (2019) Survey analysis of automatic detection and grading of cataract using different imaging modalities. Applications of Intelligent Technologies in Healthcare Springer. https://www.springerprofessional.de/en/survey-analysis-of-automatic-detection-and-grading-of-cataract-u/16262990
Ye Y, Wang J, Xie Y et al (2014) Global teleophthalmology with iPhones for real-time slitlamp eye examination. Eye Contact Lens 40:297–300
Yousaf B, Usama M, Sultani W, Mahmood A, Qadir J (2022) Fake visual content detection using two-stream convolutional neural networks. Neural Comput Appl 34:1–14. https://doi.org/10.1007/s00521-022-06902-5
Zhou ZH, Li M (2005) Tri-training: exploiting unlabeled data using three classifiers. IEEE Trans Knowl Data Eng 17(11):1529–1541. https://doi.org/10.1109/TKDE.2005.186
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Kalyani, B.J.D., Hemavathi, U., Meena, K. et al. Smart cataract detection system with bidirectional LSTM. Soft Comput 27, 7525–7533 (2023). https://doi.org/10.1007/s00500-023-07879-6
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DOI: https://doi.org/10.1007/s00500-023-07879-6