Yıl 2021, Cilt 9 , Sayı 1, Sayfalar 47 - 52 2021-01-29

An Expert System to Predict Eye Disorder Using Deep Convolutional Neural Network
An Expert System to Predict Eye Disorder Using Deep Convolutional Neural Network

Moahmmed Rashid AHMED [1] , Saadaldeen Rashid AHMED [2] , Adil Deniz DURU [3] , Osman Nuri UÇAN [4] , Oğuz BAYAT [5]


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.
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.
  • 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
Birincil Dil en
Konular Mühendislik
Yayınlanma Tarihi Ocak 2021
Bölüm Makaleler
Yazarlar

Orcid: 0000-0003-2833-1058
Yazar: Moahmmed Rashid AHMED
Kurum: ALTINBAŞ ÜNİVERSİTESİ
Ülke: Turkey


Orcid: 0000-0003-2259-7437
Yazar: Saadaldeen Rashid AHMED
Kurum: Tikrit University
Ülke: Iraq


Orcid: 0000-0003-3014-9626
Yazar: Adil Deniz DURU (Sorumlu Yazar)
Kurum: MARMARA ÜNİVERSİTESİ
Ülke: Turkey


Orcid: 0000-0002-4100-0045
Yazar: Osman Nuri UÇAN
Kurum: ALTINBAS UNIVERSITY
Ülke: Turkey


Orcid: 0000-0001-5988-8882
Yazar: Oğuz BAYAT
Kurum: ALTINBAŞ ÜNİVERSİTESİ
Ülke: Turkey


Tarihler

Başvuru Tarihi : 21 Mayıs 2020
Kabul Tarihi : 17 Aralık 2020
Yayımlanma Tarihi : 29 Ocak 2021

IEEE M. Ahmed , S. Ahmed , A. Duru , O. Uçan ve O. Bayat , "An Expert System to Predict Eye Disorder Using Deep Convolutional Neural Network", Academic Platform Journal of Engineering and Science, c. 9, sayı. 1, ss. 47-52, Oca. 2021, doi:10.21541/apjes.741194