Abstract
Aim: Computer-aided diagnosis of retinopathy is a research hotspot in the field of medical image classification. Optical coherence tomography (OCT) is widely applied in the diagnosis of ocular diseases. In this study, we aim to classify the images into four classes, namely CNV, DME, drusen and normal. Methodology: In this study, we present the solution to classify the OCT images using simple baseline 3, 5 and 7 layer deep convolutional neural networks (CNNs). It also explores the effect of hyperparameters such as dropout, image size, batch normalisation, epochs and their relationships with the accuracy, sensitivity and specificity of the models. Results: The novelty of this study is that it does not use any pre-trained models and yet achieves desired results just by hyperparameter tuning and some clever observations. The best results were yielded by 5 layer model having hyperparameters image size 64 × 64, 30 epochs with dropout and batch normalisation achieving an accuracy of 97.92%. The biggest risk of overfitting in deep learning where multiple layered models are trained and tested and approaches of diminishing the overfitting effect has been discussed in detail.
Kushwaha and Rastogi authors have contributed equally to the work.
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References
K.D. Schick, N.P. Toth, Making Silent Stones Speak: Human Evolution and the Dawn of Technology (Simon and Schuster, 1994)
T. Taylor, The Artificial Ape: How Technology Changed the Course of Human Evolution (St. Martin’s Press, 2010)
S.R. Palumbi, Humans as the world’s greatest evolutionary force. Science 293(5536), 1786–1790 (2001)
S. Khan, T. Yairi, A review on the application of deep learning in system health management. Mech. Syst. Signal Process. 107, 241–265 (2018)
N. Bansal, A. Sharma, R.K. Singh, A review on the application of deep learning in legal domain, in IFIP International Conference on Artificial Intelligence Applications and Innovations (Springer, Cham, 2019, May), pp. 374–381
R. Singh, S. Srivastava, Stock prediction using deep learning. Multimedia Tools Appl. 76(18), 18569–18584 (2017)
D. Huang, E.A. Swanson, C.P. Lin, J.S. Schuman, W.G. Stinson, W. Chang, M.R. Hee, T. Flotte, K. Gregory, C.A. Puliafito, Optical coherence tomography. Science 254(5035), 1178–1181 (1991)
C.A. Puliafito, M.R. Hee, C.P. Lin, E. Reichel, J.S. Schuman, J.S. Duker, J.A. Izatt, E.A. Swanson, J.G. Fujimoto, Imaging of macular diseases with optical coherence tomography. Ophthalmology 102(2), 217–229 (1995)
K. Horie-Inoue, S. Inoue, Genomic aspects of age-related macular degeneration. Biochem. Biophys. Res. Commun. 452(2), 263–275 (2014)
J. Merl-Pham, F. Gruhn, S.M. Hauck, Proteomic profiling of cigarette smoke induced changes in retinal pigment epithelium cells, in Retinal Degenerative Diseases (Springer, Cham, 2016), pp. 785–791
D. Iejima, M. Nakayama, T. Iwata, HTRA1 overexpression induces the exudative form of age-related macular degeneration. J. Stem Cells 10(3), 193 (2015)
M.R. Hee, J.A. Izatt, E.A. Swanson, D. Huang, J.S. Schuman, C.P. Lin, C.A. Puliafito, J.G. Fujimoto, Optical coherence tomography of the human retina. Arch. Ophthalmol. 113(3), 325–332 (1995)
J.R. Evans, J.G. Lawrenson, Antioxidant vitamin and mineral supplements for slowing the progression of age-related macular degeneration. Cochrane Database Syst. Rev. 7 (2017)
G. Gregori, F. Wang, P.J. Rosenfeld, Z. Yehoshua, N.Z. Gregori, B.J. Lujan, C.A. Puliafito, W.J. Feuer, Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration. Ophthalmology 118(7), 1373–1379 (2011)
M.M. Engelgau, L.S. Geiss, J.B. Saaddine, J.P. Boyle, S.M. Benjamin, E.W. Gregg, E.F. Tierney, N. Rios-Burrows, A.H. Mokdad, E.S. Ford, G. Imperatore, The evolving diabetes burden in the United States. Ann. Intern. Med. 140(11), 945–950 (2004)
R.J. Tapp, J.E. Shaw, C.A. Harper, M.P. De Courten, B. Balkau, D.J. McCarty, H.R. Taylor, T.A. Welborn, P.Z. Zimmet, The prevalence of and factors associated with diabetic retinopathy in the Australian population. Diabetes Care 26(6), 1731–1737 (2003)
P.J. Kertes, T.M. Johnson (eds.), Evidence-Based Eye Care (Lippincott Williams & Wilkins, 2007)
L.V. Johnson, W.P. Leitner, M.K. Staples, D.H. Anderson, Complement activation and inflammatory processes in Drusen formation and age related macular degeneration. Exp. Eye Res. 73(6), 887–896 (2001)
L.V. Johnson, S. Ozaki, M.K. Staples, P. Erickson, D.H. Anderson, A potential role for immune complex pathogenesis in drusen formation. Experi. Eye Res. 70(4), 441–449 (2000)
H.E. Grossniklaus, W.R. Green, Choroidal neovascularization. Am. J. Ophthalmol. 137(3), 496–503 (2004)
F. Li, H. Chen, Z. Liu, X. Zhang, M. Jiang, Z. Wu, K. Zhou, Deep learning-based automated detection of retinal diseases using optical coherence tomography images. Biomed. Opt. Express 10, 6204–6226 (2019)
W. Lu, et al., Deep learning-based automated classification of multi-categorical abnormalities from optical coherence tomography images. Transl. Vis. Sci. Technol. 7(6), 41 (2018). https://doi.org/10.1167/tvst.7.6.41
Y. Wang, et al., machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images. Biomed. Opt. Express 7(12), 4928 (2016). https://doi.org/10.1364/boe.7.004928
P.P. Srinivasan, et al., Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed. Opt. Express 5(10), 3568 (2014). https://doi.org/10.1364/boe.5.003568
D. Kermany, M. Goldbaum, W. Cai, C. Valentim, H. Liang, S. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. Huu, C. Wen, E. Zhang, C. Zhang, O. Li, X. Wang, M. Singer, X. Sun, J. Xu, A. Tafreshi, M. Lewis, H. Xia, K. Zhang, Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122-1131.e9 (2018)
C.S. Lee, et al., Deep learning is effective for the classification of OCT images of normal versus age-related macular degeneration (2016). https://doi.org/10.1101/094276
G.C.Y. Chan, et al., Fusing results of several deep learning architectures for automatic classification of normal and diabetic macular edema in optical coherence tomography, in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018. https://doi.org/10.1109/embc.2018.8512371
Z. Zhang, M.W. Beck, D.A. Winkler, B. Huang, W. Sibanda, H. Goyal, Opening the black box of neural networks: methods for interpreting neural network models in clinical applications. Annal. Transl. Med. 6(11) (2018)
Y. Bengio, Deep learning of representations for unsupervised and transfer learning, in Proceedings of ICML Workshop on Unsupervised and Transfer Learning (2012, June), pp. 17–36
H.W. Ng, V.D. Nguyen, V. Vonikakis, S. Winkler, Deep learning for emotion recognition on small datasets using transfer learning, in Proceedings of the 2015 ACM on International Conference On Multimodal Interaction (2015, Nov), pp. 443–449
R. Raina, A. Battle, H. Lee, B. Packer, A.Y. Ng, Self-taught learning: transfer learning from unlabeled data, in Proceedings of the 24th International Conference on Machine Learning (2007, June), pp. 759–766
K. Gopalakrishnan, S.K. Khaitan, A. Choudhary, A. Agrawal, Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection. Constr. Build. Mater. 157, 322–330 (2017)
S. Ruder, M.E. Peters, S. Swayamdipta, T. Wolf, Transfer learning in natural language processing, in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials (2019, June), pp. 15–18
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Kushwaha, A.K., Rastogi, S. (2022). Solution to OCT Diagnosis Using Simple Baseline CNN Models and Hyperparameter Tuning. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1394. Springer, Singapore. https://doi.org/10.1007/978-981-16-3071-2_30
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