Abstract
Glaucoma according to the W.H.O is one of the major causes of blindness worldwide. Due to its complexity and silent nature early detection of this disease makes it hard to detect. There have been several techniques over the years for classification which have shown significant improvement over the past decade or two. Some of the many classification models are SVM (support vector machine), KNN (K- Nearest Neighbors), Decision tree, Logistic Regression and ANN (Artificial Neural Network) back propagation. For this paper we would consider different procedure and method of early detection of the glaucoma disease using the MATLAB Deep Convolutional Neural Network (DCNN). The DCNN based expert system basically works like the human brain with input, neurons, hidden layers and output. For this project Fundus image of both healthy image and glaucoma image are collected with good lighting condition so that all hidden features can be identify. The Fundus image are then passed through different image processing method such as Grayscale, B&W, Complement, Robert, Resize and power Transform. The fundus is then passed through a texture feature extraction algorithm know as Deep Convolutional Neural Network (DCNN). The features gotten are Contrast, Correlation, energy, Homogeneity, Entropy, Mean, Standard deviation, Variance, skewness and Kurtosis. After the feature extraction the data are arrangement on a spreadsheet which serves as a means of record. Lastly, a deep convolutional neural network is written with one hidden layer, 16 input neuron and 2 output either healthy or not. The data are split into train and test dataset with 70% for training 15% validation and 15% for testing. Accuracy of detection was 92.78% with the execution time of 5.33s only depending on the number of iteration or epochs.
References
- Xu, Y., Lin, S., Wong, T.Y., Liu, J., Xu, D.: Efficient ReconstructionBased Optic Cup Localization for Glaucoma Screening. In: MICCAI 2013
- C. Bowd, R. N. Weinreb, J. M. Williams, and L. M. Zangwill, “The retinal nerve fiber layer thickness glaucomatous eyes with galucoma optical coherence tomography on Fundus and normal Images,” Archives of ophthalmology, vol. 118, no. 1, pp. 22–26, 2017.
- Space healthcare. Understanding glaucoma. http://space-healthcare.com/wp-content/uploads/2015/02/glaucoma-300x197.jpg.
- Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. In: Arxiv 2012
- Ciresan, D.C., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: High-performance neural networks for visual object classification. In: Arxiv 2011
- Xu, Y., Xu, D., Lin, S., Liu, J., Cheng, J., Cheung, C.Y., Aung, T., Wong, T.Y.: Sliding Window and Regression based Cup Detection in Digital Fundus Images for Glaucoma Diagnosis. In: MICCAI 2011
- R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Optic nerve head of non-glaucoma and glaucoma eye via gradient-based localization,” in Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626, 2017.
- P. J. Foster, R. Buhrmann, H. A. Quigley, and G. J. Johnson, “The definition and classification of glaucoma in prevalence surveys,” British journal of ophthalmology, vol. 86, no. 2, pp. 238–242, 2015.
- S. Ahmed, O. Uçan, Adil Deniz Duru, Oğuz Bayat, “Breast Cancer Detection And Image Evaluation Using Augmented Deep Convolutional Neural Networks”, Aurum Mühendislik Sistemleri ve Mimarlık Dergisi, Vol. 2, No. 2, Pp. 121–129, 2018.
- J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. ODonoghue, D. Visentin, et al., “Clinically applicable deep learning for diagnosis and referral in retinal disease,” Nature medicine, vol. 24, no. 9, p. 1342, 2018.
- F. Iandola, M. Moskewicz, S. Karayev, R. Girshick, T. Darrell, and K. Keutzer, “Densenet: Implementing efficient convnet descriptor pyramids,” arXiv preprint arXiv:1404.1869, 2014.
- Y. Wu and K. He, “Group normalization,” in Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19, 2018. Available Online: https://www.kaggle.com/kondwani/eye-disease-dataset.
- Available Online: https://www.kaggle.com/kondwani/eye-disease-dataset.
- Malik, S.; Kanwal, N.; Asghar, M.N.; Sadiq, M.A.A.; Karamat, I.; Fleury, M. Data Driven Approach for Eye Disease Classification with Machine Learning. Appl. Sci. 2019, 9, 2789.
- D. G. Duru and A. D. Duru, "MR-MS Image Classification based on Convolutional Neural Networks," 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), Istanbul, Turkey, 2019, pp. 1-4, doi: 10.1109/EBBT.2019.8741752.
- D. G. Duru and A. D. Duru, "Classification of Event Related Potential Patterns using Deep Learning," 2018 Medical Technologies National Congress (TIPTEKNO), Magusa, 2018, pp. 1-4, doi: 10.1109/TIPTEKNO.2018.8597016
Abstract
Glaucoma according to the W.H.O is one of the major causes of blindness worldwide. Due to its complexity and silent nature early detection of this disease makes it hard to detect. There have been several techniques over the years for classification which have shown significant improvement over the past decade or two. Some of the many classification models are SVM (support vector machine), KNN (K- Nearest Neighbors), Decision tree, Logistic Regression and ANN (Artificial Neural Network) back propagation. For this paper we would consider different procedure and method of early detection of the glaucoma disease using the MATLAB Deep Convolutional Neural Network (DCNN). The DCNN based expert system basically works like the human brain with input, neurons, hidden layers and output. For this project Fundus image of both healthy image and glaucoma image are collected with good lighting condition so that all hidden features can be identify. The Fundus image are then passed through different image processing method such as Grayscale, B&W, Complement, Robert, Resize and power Transform. The fundus is then passed through a texture feature extraction algorithm know as Deep Convolutional Neural Network (DCNN). The features gotten are Contrast, Correlation, energy, Homogeneity, Entropy, Mean, Standard deviation, Variance, skewness and Kurtosis. After the feature extraction the data are arrangement on a spreadsheet which serves as a means of record. Lastly, a deep convolutional neural network is written with one hidden layer, 16 input neuron and 2 output either healthy or not. The data are split into train and test dataset with 70% for training 15% validation and 15% for testing. Accuracy of detection was 92.78% with the execution time of 5.33s only depending on the number of iteration or epochs.
References
- Xu, Y., Lin, S., Wong, T.Y., Liu, J., Xu, D.: Efficient ReconstructionBased Optic Cup Localization for Glaucoma Screening. In: MICCAI 2013
- C. Bowd, R. N. Weinreb, J. M. Williams, and L. M. Zangwill, “The retinal nerve fiber layer thickness glaucomatous eyes with galucoma optical coherence tomography on Fundus and normal Images,” Archives of ophthalmology, vol. 118, no. 1, pp. 22–26, 2017.
- Space healthcare. Understanding glaucoma. http://space-healthcare.com/wp-content/uploads/2015/02/glaucoma-300x197.jpg.
- Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. In: Arxiv 2012
- Ciresan, D.C., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: High-performance neural networks for visual object classification. In: Arxiv 2011
- Xu, Y., Xu, D., Lin, S., Liu, J., Cheng, J., Cheung, C.Y., Aung, T., Wong, T.Y.: Sliding Window and Regression based Cup Detection in Digital Fundus Images for Glaucoma Diagnosis. In: MICCAI 2011
- R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Optic nerve head of non-glaucoma and glaucoma eye via gradient-based localization,” in Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626, 2017.
- P. J. Foster, R. Buhrmann, H. A. Quigley, and G. J. Johnson, “The definition and classification of glaucoma in prevalence surveys,” British journal of ophthalmology, vol. 86, no. 2, pp. 238–242, 2015.
- S. Ahmed, O. Uçan, Adil Deniz Duru, Oğuz Bayat, “Breast Cancer Detection And Image Evaluation Using Augmented Deep Convolutional Neural Networks”, Aurum Mühendislik Sistemleri ve Mimarlık Dergisi, Vol. 2, No. 2, Pp. 121–129, 2018.
- J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. ODonoghue, D. Visentin, et al., “Clinically applicable deep learning for diagnosis and referral in retinal disease,” Nature medicine, vol. 24, no. 9, p. 1342, 2018.
- F. Iandola, M. Moskewicz, S. Karayev, R. Girshick, T. Darrell, and K. Keutzer, “Densenet: Implementing efficient convnet descriptor pyramids,” arXiv preprint arXiv:1404.1869, 2014.
- Y. Wu and K. He, “Group normalization,” in Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19, 2018. Available Online: https://www.kaggle.com/kondwani/eye-disease-dataset.
- Available Online: https://www.kaggle.com/kondwani/eye-disease-dataset.
- Malik, S.; Kanwal, N.; Asghar, M.N.; Sadiq, M.A.A.; Karamat, I.; Fleury, M. Data Driven Approach for Eye Disease Classification with Machine Learning. Appl. Sci. 2019, 9, 2789.
- D. G. Duru and A. D. Duru, "MR-MS Image Classification based on Convolutional Neural Networks," 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), Istanbul, Turkey, 2019, pp. 1-4, doi: 10.1109/EBBT.2019.8741752.
- D. G. Duru and A. D. Duru, "Classification of Event Related Potential Patterns using Deep Learning," 2018 Medical Technologies National Congress (TIPTEKNO), Magusa, 2018, pp. 1-4, doi: 10.1109/TIPTEKNO.2018.8597016
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Publication Date | January 29, 2021 |
| Submission Date | May 21, 2020 |
| Published in Issue | Year 2021 Volume: 9 Issue: 1 |
