Automatic Cataract Classification Using Deep Neural Network With Discrete State Transition

Cataract is the clouding of lens, which affects vision and it is the leading cause of blindness in the world’s population. Accurate and convenient cataract detection and cataract severity evaluation will improve the situation. Automatic cataract detection and grading methods are proposed in this paper. With prior knowledge, the improved Haar features and visible structure features are combined as features, and multilayer perceptron with discrete state transition (DST-MLP) or exponential DST (EDST-MLP) are designed as classifiers. Without prior knowledge, residual neural networks with DST (DST-ResNet) or EDST (EDST-ResNet) are proposed. Whether with prior knowledge or not, our proposed DST and EDST strategy can prevent overfitting and reduce storage memory during network training and implementation, and neural networks with these strategies achieve state-of-the-art accuracy in cataract detection and grading. The experimental results indicate that combined features always achieve better performance than a single type of feature, and classification methods with feature extraction based on prior knowledge are more suitable for complicated medical image classification task. These analyses can provide constructive advice for other medical image processing applications.

[1]  Thomas Walter,et al.  Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques , 2001, ISMDA.

[2]  Jiang Liu,et al.  Image based diagnosis of cortical cataract , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Song Han,et al.  Trained Ternary Quantization , 2016, ICLR.

[4]  M. Goldbaum,et al.  Detection of blood vessels in retinal images using two-dimensional matched filters. , 1989, IEEE transactions on medical imaging.

[5]  Charles R. Dyer,et al.  An Automatic System for Classification of Nuclear Sclerosis from Slit-Lamp Photographs , 2003, MICCAI.

[6]  Elisa Ricci,et al.  Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification , 2007, IEEE Transactions on Medical Imaging.

[7]  Li Xiong,et al.  An Approach to Evaluate Blurriness in Retinal Images with Vitreous Opacity for Cataract Diagnosis , 2017, Journal of healthcare engineering.

[8]  Jianqiang Li,et al.  Principal component analysis based cataract grading and classification , 2015, 2015 17th International Conference on E-health Networking, Application & Services (HealthCom).

[9]  Tien Yin Wong,et al.  Automatic Grading of Cortical and PSC Cataracts Using Retroillumination Lens Images , 2012, ACCV.

[10]  Jianqiang Li,et al.  A computer-aided healthcare system for cataract classification and grading based on fundus image analysis , 2015, Comput. Ind..

[11]  Shenghuo Zhu,et al.  Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM , 2017, AAAI.

[12]  Jianqiang Li,et al.  Exploiting ensemble learning for automatic cataract detection and grading , 2016, Comput. Methods Programs Biomed..

[13]  L T Chylack,et al.  Evaluation of lens opacities classification system III applied at the slitlamp. , 1993, Optometry and vision science : official publication of the American Academy of Optometry.

[14]  Ali Farhadi,et al.  XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks , 2016, ECCV.

[15]  Xu Lian.  A new method of cataract imaging evaluation , 2010 .

[16]  Lei Deng,et al.  Training deep neural networks with discrete state transition , 2018, Neurocomputing.

[17]  P. Burlina,et al.  Automated classification of severity of age-related macular degeneration from fundus photographs. , 2013, Investigative ophthalmology & visual science.

[18]  A. Foster.  Vision 2020: the cataract challenge. , 2000, Community eye health.

[19]  David Allen,et al.  Cataract and surgery for cataract , 2006, BMJ : British Medical Journal.

[20]  D. Pascolini,et al.  Global estimates of visual impairment: 2010 , 2011, British Journal of Ophthalmology.

[21]  Yoshua Bengio,et al.  BinaryConnect: Training Deep Neural Networks with binary weights during propagations , 2015, NIPS.

[22]  Huiqi Li,et al.  Hierarchical method for cataract grading based on retinal images using improved Haar wavelet , 2019, Inf. Fusion.

[23]  U. Rajendra Acharya,et al.  Wavelet-Based Energy Features for Glaucomatous Image Classification , 2012, IEEE Transactions on Information Technology in Biomedicine.

[24]  Deguang Han,et al.  On the Orthogonality of Frames and the Density and Connectivity of Wavelet Frames , 2009 .

[25]  Shuang Wu,et al.  Training and Inference with Integers in Deep Neural Networks , 2018, ICLR.

[26]  L. Chylack,et al.  Classification of human senile cataractous changes by the American Cooperative Cataract Research Group (CCRG) method. I. Instrumentation and technique. , 1983, Investigative ophthalmology & visual science.

[27]  Geoffrey E. Hinton,et al.  Experiments on Learning by Back Propagation. , 1986 .

[28]  A. Molteno,et al.  Fourier analysis of digital retinal images in estimation of cataract severity , 2008, Clinical & experimental ophthalmology.

[29]  Tien Yin Wong,et al.  Automatic detection of posterior subcapsular cataract opacity for cataract screening , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[30]  Jianqiang Li,et al.  Automatic cataract detection and grading using Deep Convolutional Neural Network , 2017, 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC).

[31]  Andrew Hunter,et al.  Measurement of retinal vessel widths from fundus images based on 2-D modeling , 2004, IEEE Transactions on Medical Imaging.

[32]  Ran El-Yaniv,et al.  Binarized Neural Networks , 2016, NIPS.

[33]  Wang Ya.  Cataract screening as a key step to vision restoring project in country , 2010 .

[34]  Hiroshi Fujita,et al.  Automated segmentation of optic disc region on retinal fundus photographs: Comparison of contour modeling and pixel classification methods , 2011, Comput. Methods Programs Biomed..

[35]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Song Xu-don,et al.  The study of predicting the visual acuity after phacoemulsification according to the blur level of fundus photography , 2010 .

[37]  Jana Kosecka,et al.  Deep Convolutional Features for Image Based Retrieval and Scene Categorization , 2015, ArXiv.

[38]  Bram van Ginneken,et al.  Automatic detection of red lesions in digital color fundus photographs , 2005, IEEE Transactions on Medical Imaging.

[39]  Joo-Hwee Lim,et al.  A Computer-Aided Diagnosis System of Nuclear Cataract , 2010, IEEE Transactions on Biomedical Engineering.

[40]  A. Haar.  Zur Theorie der orthogonalen Funktionensysteme , 1910 .

[41]  Richard M. Davis,et al.  Cataract Screening Using Telemedicine and Digital Fundus Photography , 2005 .

[42]  Zhenzhi Wu,et al.  GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework , 2017, Neural Networks.

[43]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[44]  Jianqiang Li,et al.  Classification of retinal image for automatic cataract detection , 2013, 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013).

[45]  Aliaa A. A. Youssif,et al.  Optic Disc Detection From Normalized Digital Fundus Images by Means of a Vessels' Direction Matched Filter , 2008, IEEE Transactions on Medical Imaging.

[46]  U. Rajendra Acharya,et al.  Computer-aided diagnosis of diabetic retinopathy: A review , 2013, Comput. Biol. Medicine.

[47]  Joo-Hwee Lim,et al.  Image based grading of nuclear cataract by SVM regression , 2008, SPIE Medical Imaging.

[48]  Qin Li,et al.  Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs , 2010, IEEE Transactions on Medical Imaging.

[49]  A. R. Hill,et al.  The Oxford Clinical Cataract Classification and Grading System , 1986, International Ophthalmology.