Abstract
The execution of medical decision-making or clinical diagnosis in medical or clinical imaging generally involves difficulties with reliability, robustness, and interpretability of data. In this project, we setup an analytic and predictive system dependent on a deep learning technique for the screening of patients with treatable blinding retinal illnesses, specifically related to choroidal neovascularization (CNV), diabetic macular edema, DRUSEN, or normal retina. We use the power of transfer learning in our neural network, which prepares our deep-learning model to perform better on X-ray retinal images. When a dataset of optical coherence tomography (OCT) pictures is given to a model, we are able to exhibit results comparable to that of human specialists in characterizing and segregating age-related illnesses of the retina, especially corresponding to choroidal neovascularization, macular degeneration, and diabetic macular edema. Transfer learning leverages the power of pre-trained weights and biases to allow a deep learning neural network to find better patters and classify more efficiently. This system may assist in the conclusion and reference of treatable retinal diseases and eye-related conditions, consequently allowing early detection and treatment of illnesses, bettering medical treatment of patients.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kermany DS, Goldbaum M, Cai W, Anthony Lewis MXia H, Zhang K (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning
Bressler NM, Cabrera DeBuc D, Burlina P (2019) Deep learning based retinal OCT segmentation. https://arxiv.org/pdf/1801.09749.pdf
Duan, Xi D, Zhu Y, Zhu H, IEEE, Hui Xiong, Fellow, IEEE, and Qing He
Kshitiz K et al (2017) Detecting hate speech and insults on social commentary using nlp and machine learning. Int J Eng Technol Sci Res 4(12):279–285
Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 10:1345–1359
Wang Z, Dai Z, Poczos B, Carbonell J (2019) Characterizing and avoiding negative transfer. In: Proc. IEEE conference on computer vision and pattern recognition. Long Beach 11293–11302
Kumar S, Dubey S, Gupta P (2015) Auto-selection and management of dynamic SGA parameters in RDBMS. In: 2015 2nd international conference on computing for sustainable global development (INDIACom), pp 1763–1768
Huang J, Smola AJ, Gretton A, Borgwardt KM, Sch¨olkopf B (2006) Correcting sample selection bias by unlabeled data. In: Proc. 20th annual conference on neural information processing systems, Vancouver, pp 601–608
Sugiyama M, Suzuki T, Nakajima S, Kashima H, Bnau P, Kawanabe M (2008) Direct importance estimation for covariate shift adaptation. Ann Inst Stat Math 60(4):699–746
Tian J, Varga B, Tatrai E, Fanni P, Somfai GM, Smiddy WE, DeBuc DC (2016) Performance evaluation of automated segmentation software on optical coherence tomography volume data. J Biophotonics 9(5):478–489
Klein R, Klein BEK (2013) The prevalence of age-related eye diseases and visual impairment in aging: current estimates. Invest Ophthalmol Vis Sci 54(14)
Biswas R et al (2012) A framework for automated database tuning using dynamic SGA parameters and basic operating system utilities. Database Syst J III(4)
Kumar SH (2013) E-COCOMO: the extended cost constructive model for cleanroom software engineering. Database Syst J 4(4):3–11
Bird AC, Bressler NM, Bressler SB, Chisholm IH, Coscas G, Davis MD, De Jong PTVM, Klaver CCW, Klein BE, Klein R et al (1995) An international classification and grading system for age-related maculopathy and age-related macular degeneration. Survey Ophthalmol 39(5):367–374
Bressler NM (2004) Age-related macular degeneration is the leading cause of blindness... JAMA 291(15):1900–1901
Khanchi I, Agarwal N, Seth P (2019) Real time activity logger: a user activity detection system. Int J Eng Adv Technol 9(1):1991–1994
Bhushan A, Rastogi P (2017) I/O and memory management: two keys for tuning RDBMS. In: Proceedings on 2016 2nd international conference on next generation computing technologies, NGCT 2016 7877416, pp 208–214
Jaffe GJ, Martin DF, Toth CA, Daniel E, Maguire MG, Ying G-H, Grunwald JE, Huang J, Comparison of Agerelated Macular Degeneration Treatments Trials Research Group et al (2013) Macular morphology and visual acuity in the comparison of age-related macular degeneration treatments trials. Ophthalmology 120(9):1860–1870
Khanna A, Sah A, Choudhury T (2020) Intelligent mobile edge computing: a deep learning based approach. In: Singh M, Gupta P, Tyagi V, Flusser J, Ören T, Valentino G (eds) Advances in computing and data sciences. ICACDS 2020. Communications in Computer and Information Science, vol 1244. Springer, Singapore. https://doi.org/10.1007/978-981-15-6634-9_11
Sille R, Choudhury T, Chauhan P, Sharma D (2021) A systematic approach for deep learning based brain tumor segmentation. Ingénierie des Systèmes d'Information
Vashishtha P, Choudhury T (2020) Procedia computer science, 2020, DMAPS: an effective and efficient way for the air purification of the outdoors:(Deep-mind Air Purification System for a smart city)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sharma, H.K., Choudhary, R., Kumar, S., Choudhury, T. (2023). Retinal Optical Coherence Tomography Classification Using Deep Learning. In: Dutta, P., Chakrabarti, S., Bhattacharya, A., Dutta, S., Shahnaz, C. (eds) Emerging Technologies in Data Mining and Information Security. Lecture Notes in Networks and Systems, vol 490. Springer, Singapore. https://doi.org/10.1007/978-981-19-4052-1_24
Download citation
DOI: https://doi.org/10.1007/978-981-19-4052-1_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-4051-4
Online ISBN: 978-981-19-4052-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)