Clinical Decision Support System for Ophthalmologists for Eye Disease Classification

References

• P. P. Srinivasan, L. A. Kim, P. S. Mettu, S. W. Cousins, G. M. Comer, J. A. Izatt, and S. Farsiu, “Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images,” Biomed. Opt. Express, Vol. 5, no. 10, pp. 3568–77, Oct. 2014. DOI:10.1364/BOE.5.003568
   PubMed Web of Science ®Google Scholar
• J. Deng, X. Xie, L. Terry, A. Wood, N. White, T. H. Margrain, and R. V. North, “Age-related macular degeneration detection and stage classification using choroidal OCT images,” in International Conference on Image Analysis and Recognition (ICIAR), Jul. 2016, pp. 707–15.
   Google Scholar
• A. Kunwar, S. Magotra, and M. Partha Sarathi, “Detection of high-risk macular edema using texture features and classification using SVM classifier,” in International Conference on Advances in Computing, Communications and Informatics (ICACCI), Aug 2015. pp. 2285–9.
   Google Scholar
• T. Schlegl, et al., “Fully automated detection and quantification of macular fluid in OCT using deep learning,” Am. Acad. Ophthalmol., Vol. 25, no. 4, pp. 549–58, Apr. 2018.
   Google Scholar
• S. P. K. Karri, D. Chakraborty, and J. Chatterjee, “Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration,” Biomed. Opt. Express, Vol. 8, no. 2, pp. 579–92, Feb. 2017. DOI:10.1364/BOE.8.000579
   PubMed Web of Science ®Google Scholar
• O. Perdomo, S. Otálora, F. A. González, F. Meriaudeau, and H. Müller, “OCT-NET: A convolutional network for automatic classification of normal and diabetic macular edema using SD-OCT volumes,”  in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI), Apr. 2018.
   Google Scholar
• R. M. Kamble, G. C. Y. Chan, O. Perdomo, F. A. González, M. Kokare, H. Müller, and F. Mériaudeau, “Automated diabetic macular edema (DME) analysis using fine tuning with Inception-Resnet-v2 on OCT images,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Jul. 2018, pp. 1–4.
   Google Scholar
• S. P. K. Karri1, D. bjani Chakraborty, and J. Chaterjee, “Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration,” Biomed. Opt. Express, Vol. 8, no. 2, pp. 579–92, Feb. 2017. DOI:10.1364/BOE.8.000579
   PubMedGoogle Scholar
• G. C. Y. Chan, A. Muhammad, S. A. A. Shah, T. B. Tang, C.-K. Lu, and F. Meriaudeau, “Transfer learning for diabetic macular edema (DME) detection on optical coherence tomography (OCT) images,” in Proc. of the 2017 IEEE International Conference on Signal and Image Processing Applications (IEEE ICSIPA 2017), Malaysia, Sep. 2017, pp. 493–6.
   Google Scholar
• F. Grassmann, et al., “A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography,” Am. Acad. Ophthalmol, Vol.125, no.9 , pp. 78–90, Apr. 2018.
   Google Scholar
• G. C. Y. Chan, A. Muhammad, S. A. A. Shah, T. B. Tang, C.-K. Lu, and F. Meriaudeau, “Transfer learning for diabetic macular edema (DME) detection on optical coherence tomography (OCT) images,” in Proc. of the 2017 IEEE International Conference on Signal and Image Processing Applications (IEEE ICSIPA 2017), Sep. 12–14, 2017, pp. 493–6.
   Google Scholar
• U. K. Lopes and J. F. Valiati, “Pre-trained convolutional neural networks as feature extractors for tuberculosis detection,” Comput. Biol. Med., Vol. 89, no. 1, pp. 135–43, Aug. 2017. DOI:10.1016/j.compbiomed.2017.08.001
   PubMedGoogle Scholar
• S. Rajaraman, et al., “Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images,” PeerJ, Vol. 6, pp. e4568, Apr. 2018. DOI:10.7717/peerj.4568.
   PubMed Web of Science ®Google Scholar
• P. Burlina, K. D. Pacheco, N. Joshi, D. E. Freund, and N. M. Bressler, “Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis,” Comput. Biol. Med., Vol. 82, no. 1, pp. 80–6, Mar. 2017.
   PubMedGoogle Scholar
• R. Zhaq, et al., “Automated drusen detection in dry age-related macular degeneration by multiple-depth, en face optical Coherence tomography,” Biomed. Opt. Express, Vol. 8, no. 11, pp. 5049–5064, Nov. 2017.
   PubMedGoogle Scholar
• J. M. Patterson, A. Hildreth, and J. A. Wilson, “Measuring edema in irradiated head and neck cancer patients” Ann. Otol. Rhinol. Laryngol., Vol. 116, no. 8, pp. 559–64, Aug. 2007. DOI:10.1177/000348940711600801
   PubMed Web of Science ®Google Scholar
• S. Vellakani and I. Pushbam, “An enhanced OCT image captioning system to assist ophthalmologist in detecting and classifying eye diseases,” J. Xray. Sci. Technol., Vol. 28, no. 5, pp. 975–88, Sep. 2020.
   PubMedGoogle Scholar
• Available: https://github.com/sivamuruganv/IETE-Journal.git
   Google Scholar