Classification of Corneal Pattern Based on Convolutional LSTM Neural Network | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 51, Volume 28, ICEEM2019-Special Issue, 2019, Page 158-162 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2019.76998 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Nehad T. Haggag1; Ahmed Sedik2; Ghada M. Elbanby3; Adel S. El-Fishawy4; Moawad I- Dessouky4; Ashraf A. M. Khalaf5 | ||||
| 1Department of Electronics and Electrical Communications EngineeringFaculty of Electronic Engineering Menoufia University: Menouf, Egypt. | ||||
| 2Dep. of The Robotics and Intelligent Machines , Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt | ||||
| 3Dep. of Ind. Electronics and Control Eng., Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt | ||||
| 4Dept. of Electronics and electrical communication, Faculty of electronic Engineering, Menoufia University, Menouf, Egypt. | ||||
| 5Department of Electronics and Electrical Communications Engineering Faculty of Engineering Minia University: Minia, EgyptCity, Country | ||||
| Abstract | ||||
| The development of the image classification techniques using deep learning has become one of the interesting research fields. It can be used in several fields such as the diagnosis of the corneal diseases. This paper proposes a Convolutional neural network – Long short-term memory (CNN-LSTM) model that can classifies the corneal images into normal and abnormal cases. The experimental results reveal that the CNN-LSTM neural network model provides a high performance. This model combines convolutional neural network (CNN) and long short-term memory (LSTM). The target of this combination is to extract complex features from the corneal images with a few number of layers rather than Convolutional neural networks. The proposed technique is carried out on a set of corneal images. These images are collected from patients via confocal microscopy. The CNN-LSTM classification results on corneal fundus images achieved an accuracy of 100 %. | ||||
| Keywords | ||||
| Deep learning; Convolutional neural network (CNN); Long short Term Memory (LSTM); Normal and abnormal corneal images and model accuracy | ||||
| References | ||||
|
1] M. Balasubramian and A. Louise and Roger W “Fractal Dimension Based Corneal Fungal Diagnosis”, Proceedings of the SPIE, Volume 6312, pp. 200-211, 2006.
[2]Anna Fabijan “Corneal Endothelium Image Segmentation Using Feedforward Neural Network”, proceeding of the Federated conference on computer science and information systems pp. 629-637, IEEE Cataloge Number, 2017.
[3] Curry Bucht, “Fully Automated Corneal Endothelial Morphometric Of Images Captured By Clinical Specular Microscopy”, Courant Institute of Mathematical Sciences, pp. 125-136 2009.
[4] Maolong Tang, Raj Shekhar, And David Huang “ Curvature Mapping For Detection Of Corneal Shape Abnormality”, IEEE Transactions On Medical Imageing, pp. 16-25, March 2005.
[5] V. Girisha and K.Vijay Chandra “Machine Intelligence On Confocal Microscope For Detection Of Endothelium Layer Of Corneal Disease”, Proceeding of IJCTER,pp. 148-211 Auguest 2016.
[6] Iman Mohammed Rezazadeh and Keivan Maghooli “Investigating And Validation Numerical Measures Of Corneal Topographic Data Is Lasik Surgery Outcome Using Wavelet Transform”, Proceeding of IEEE, pp. 153-159, 2006.
[7] G.Ayala, M. Diaz And L.Martines-Costa “Granulometric Moments And Corneal Endothelium Status”, Pattern Recognition , pp.1219-1222 2001.
[8] R. Nadachi “Automated Corneal Endothelial Cell Analysis In Fifth Annual”, IEEE Symposium On Computer Based Medical Systems, ppt 14-17, 1992.
[9] F. Sanchez “ Automatic Segmentation Of Contours Of Corneal Cells”, Computers in Biology and Medicine, pp. 243-155, 1999.
[10] M. Mahzoun, K. Okazaki, H.Kawai, Y.Sato, S.Tamura, And K.Kani “ Detection And Complment Of Hexagonal Borders In Corneal Endothelial Cell Image”, Medical Imaging Technology, pp. 56-65, 1996.
[11] K.Habrat, M. Habrat “ Cell Detection In Corneal Endothelial Images Using Directional Filters”, In Image Processing And Communications Challenges , pp. 113-123, 2016.
[12] A. Piorkowski, K. Nurzynska, J. Gronkowska-Serafin, B.Selig, C.Boldak, And D. Reska “ Influence Of Applied Corneal Endothelium Image Segmentation Technigues On The Clinical Parameters, “ Comp. Med. Imag. Grap, pp. 13-27, 2017.
[13] Acharya, U Rajendra “Computer-Based Classification Of Eye Diseases”, Proceeding of the 28th IEEE EMBS annual international conferenece, New York city, USA , Aug 30-Sept 3,2006.
[14] M. Habrat, A. Piorkowski “Cell detection in corneal Endothelial images using directional filters”, October`, pp. 113-123, 2016.
[15] Alferdo Ruggeri And Enrico Grisan “Anew System For The Automatic Estimation Of Endothelial Cell Density In Donor Corneas”, Br J Ophthalmol, Ppt 306-311, 2005.
[16] Marco “Estimating Cell Density In Corneal Endothelium By Means Of Fourier Analysis”, Proceeding of IEEE, ppt 23-26, 2002.
[17] Gavet “Visual Perception Based Automatic Recognition Of Cell Mosaics In Human Corneal Endothelium Microscopy Images”, Image Anal Stereol, pp. 53-62, 2008.
[18] Enrico Grisan, Anna Paviotti “A lattice estimation approach for the automatic evaluation of corneal endothelium density” Proceeding of IEEE, ppt 150-168, 2005.
[19] G.W.Griffiths “Analysis of cornea curvature using radial basis functions-Part1-Methadology”, Computers in Biology and Medicine, Elsevier, pp.272-284, 2016.
[20] D.klyce “Computer Assisted corneal topography”, Avro-journal, pp. 210-230, 2018.
[21] A. Fabijanska, " Automatic segmentation of corneal endothelial cells from microscopy images", Biomedical Signal Processing and Control, Vol. 47, pp. 145-158, 2019.
[22] LeCun, and Y Bottou: ''Gradient-based learning applied to document recognition'', Proceedings of the IEEE, pp. 482-456, 2008.
[23] Ranzato, M. A “Unsupervised learning of invariant feature hierarchies with applications to object recognition”, IEEE Computer Vision and Pattern Recognition, pp. 845-850, 2007.
[24] Srivastava And N. Hinton Dropout “A Simple Way To Prevent Neural Networks From Over Fitting'', The Journal Of Machine Learning Research, Pp. 1929-1958, 2014.
[25] Shen, Yi. Loss functions for binary classification and class probability estimation. Diss. University of Pennsylvania, 2005.
[26] Adjogy, “Group and Crowd Behavior for Computer Vision”, Pages 183-207, 2017.
[27] Shen, Yi. “Are the long–short term memory and convolution neural networks really based on biological systems?” ICT Express
Volume 4, Pages 100-106, June 2018. | ||||
|
Statistics Article View: 197 |
||||
