Early Recognition and Grading of Cataract Using a Combined Log Gabor/Discrete Wavelet Transform with ANN and SVM (original) (raw)
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International Journal of Engineering Research and Technology (IJERT), 2016
https://www.ijert.org/a-survey-on-computer-aided-healthcare-diagnosis-for-automatic-classification-and-grading-of-cataract https://www.ijert.org/research/a-survey-on-computer-aided-healthcare-diagnosis-for-automatic-classification-and-grading-of-cataract-IJERTV5IS020268.pdf A fundus image analysis based computer aided diagnosis for automatic classification and grading of cataract is presented.The burden of ophthalmologists can be reduced due to this system and help cataract patients to know their cataract conditions. The system comprises the processes as pre-processing of fundus image, image feature extraction and automatic cataract classification and grading. A multiclass discriminant analysis algorithm is used for cataract classification, including two-class classification and cataract grading in mild, moderate, and severe. The wavelet transform is investigated to extract from fundus image. ANN based methods and SVM based methods have been used in pattern recognition and classification. The fundus image analysis for cataract classification and grading is very helpful for improving ophthalmic healthcare quality and review of fundus image.
Eye is a very important organ of the human body, which has many complex sensory elements such as lens, retina etc. Eye disorder is a prominent issue in the health care sector. Cataract is an eye disorder, which occurs due to the clouding of lens. Over a period of time, cataract will lead to reduced eyesight. If cataract is not treated in proper time, then it will lead to blindness. This is common in aged people. In this work image processing techniques are used to detect the features in the three classes of optical eye images such as normal, cataract and post-cataract images. The features of the optical eye image such as Big Ring Area (BRA), Small Ring Area (SRA), Edge Pixel Count (EPC) and Object Perimeter are extracted. The features are statistically analyzed and found to be significant for the automatic classification. The same features are then used in the automatic classifier such as Support Vector Machines (SVM) for the automatic classification. The results are found to be clinically significant with 94% sensitivity and 93.75% specificity. The classification rate is nearly 90%.
Automatic Cataract Classification on Retinal Image using Support Vector Machine
International Journal of Advanced Research in Computer Science and Software Engineering, 2017
Eye is a delicate organ of the body which provides organisms a vision. Eye is made up of sensory component such as lens, pupil, retina etc. One of the diseases which affect the human eye is cataract. Cataract occurs due to clouding of lens in the eye. Cataract is an eye disease which is responsible for vision loss and blindness. But earlier cataract detection system can provide a patient to know their condition timely and they can get the treatment accordingly. Using various image processing and classification technique one can detect and classify images. This paper points out different algorithm for detecting cataract in fundus images. This paper mainly involves mainly three steps specially preprocessing of the image, extraction of feature of preprocessed image and the last one is classification of image. In the very first step, image processing technique is applied for processing the image. We have used brightness preserving dynamic fuzzy histogram equalization method for contrast enhancement of image. In second step various feature of optical eye is extracted and the same feature are then used in classifier. For feature extraction statistical texture features such as mean, variance, energy, entropy and kurtosis of the eye is found. Support Vector Machine (SVM). SVM classification accuracy is 89%.
Multimedia Tools and Applications, 2022
This paper presents a low cost, robust, portable and automated cataract detection system which can detect the presence of cataract from the colored digital eye images and grade their severity. Ophthalmologists detect cataract through visual screening using ophthalmoscope and slit lamps. Conventionally a patient has to visit an ophthalmologist for eye screening and treatment follows the course. Developing countries lack the proper health infrastructure and face huge scarcity of trained medical professionals as well as technicians. The condition is not very satisfactory with the rural and remote areas of developed nations. To bridge this barrier between the patient and the availability of resources, current work focuses on the development of portable low-cost, robust cataract screening and grading system. Similar works use fundus and retinal images which use costly imaging modules and image based detection algorithms which use much complex neural network models. Current work derives its benefit from the advancements in digital image processing techniques. A set of preprocessing has been done on the colored eye image and later texture information in form of mean intensity, uniformity, standard deviation and randomness has been calculated and mapped with the diagnostic opinion of doctor for cataract screening of over 200 patients. For different grades of cataract severity edge Multimedia Tools and Applications
DETECTION OF CATARACT BY STATISTICAL FEATURES AND CLASSIFICATION
Cataract is the major cause of blindness in the world and the most prevalent ocular disease. Increased risk of cataract development is associated with UV exposure, steroid use, diabetes, and smoking. This process cannot be reversed, but a healthy lifestyle may slow the progression. Earlier diagnosis of cataract will prevent vision loss. In this paper a new method has been proposed to diagnosis of cataract using statistical features and its severity has been classified using Kmeans and ANFIS classifier.
Comparative analysis of image classification methods for automatic diagnosis of ophthalmic images
Scientific reports, 2017
There are many image classification methods, but it remains unclear which methods are most helpful for analyzing and intelligently identifying ophthalmic images. We select representative slit-lamp images which show the complexity of ocular images as research material to compare image classification algorithms for diagnosing ophthalmic diseases. To facilitate this study, some feature extraction algorithms and classifiers are combined to automatic diagnose pediatric cataract with same dataset and then their performance are compared using multiple criteria. This comparative study reveals the general characteristics of the existing methods for automatic identification of ophthalmic images and provides new insights into the strengths and shortcomings of these methods. The relevant methods (local binary pattern +SVMs, wavelet transformation +SVMs) which achieve an average accuracy of 87% and can be adopted in specific situations to aid doctors in preliminarily disease screening. Furthermo...
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/classification-and-grading-of-diabetic-retinal-images-for-implementation-of-computer-aided-diagnosis-system https://www.ijert.org/research/classification-and-grading-of-diabetic-retinal-images-for-implementation-of-computer-aided-diagnosis-system-IJERTV2IS80805.pdf Diabetes occurs when the pancreas fails to secrete enough insulin, slowly affecting the retina of the human eye. As it progresses, the vision of a patient starts deteriorating, leading to diabetic retinopathy. In this regard, retinal images acquired through fundal camera aid in analyzing the consequences, nature, and status of the effect of diabetes on the eye. The objectives of this study are to (i) image enhancement and denoising using Gabor filter (ii) detect blood vessel and identify the optic disc and vessel parameters and (iii) classify different stages of diabetic retinopathy into mild, moderate, severe non-proliferative diabetic retinopathy (NPDR) and proliferative Diabetic retinopathy (PDR). Computer aided diagnosis system is developed to classify and grading the retinal images using neural network and validated with various samples. Multiple features and BPN classifier is used to enhance the classification of retinal images and is helpful for ophthalmologist in efficient decision making. Classification of the different stages of eye disease was done using Back Propagation Network (BPN) technique based on the area of the exudates, micro aneurysms, and hemorrhages. Accuracy assessment of the classified output is 92.5% for the abnormal cases.
Model for Prediction of Cataracts Using Supervised Machine Learning Algorithms
This study identified the risk factors for cataracts and formulated a predictive model based on the identified variables. The study simulated the formulated model and validated the model with a view to developing a model for cataracts' risk prediction. Following the review of the body of knowledge surrounding cataracts and their corresponding risk factors, interview with mental health professionals was conducted in order to validate the identified variables. Naïve Bayes, Decision Trees and the Multi-layer Perceptron classifiers were used to formulate the predictive model for the risk of cataracts based on the identified and validated variables using the WEKA software. The results of the data collected from 31 patients revealed 9 demographic variables and 17 risk factors variables alongside the respective risk factors, yielding a total of 26 variables in all. Out of the variables identified, the C4.5 Decision Trees algorithm revealed that smoking, myopia intensity, use of lenses and frequency of alcohol consumption were the most relevant risk factors out of cataract risks in the 26 variables identified. The results also showed that out of all the supervised machine learning algorithms used, the Multi-layer Perceptron was able to predict all records (100% accuracy) of the historical dataset used while the C4.5 Decision Trees and Naïve Bayes classifiers had an accuracy of 87% and 84% respectively. The study concluded that the Multi-layer Perceptron had the best capability to identify the unseen patterns existing within the variables used to formulate the predictive model for cataract's risks.
Automated Classification of Glaucoma Images by Wavelet Energy Features
2013
Glaucoma is the second leading cause of blindness worldwide. As glaucoma progresses, more optic nerve tissue is lost and the optic cup grows which leads to vision loss. This paper compiles a system that could be used by non-experts to filtrate cases of patients not affected by the disease. This work proposes glaucomatous image classification using texture features within images and efficient glaucoma classification based on Probabilistic Neural Network (PNN). Energy distribution over wavelet sub bands is applied to compute these texture features. Wavelet features were obtained from the daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. It uses a technique to extract energy signatures obtained using 2-D discrete wavelet transform and the energy obtained from the detailed coefficients can be used to distinguish between normal and glaucomatous images. We observed an accuracy of around 95%, this demonstrates the effectiveness of these method...
Fundus Image Classification Using Wavelet Based Features in Detection of Glaucoma
Glaucoma frequently called as the " noiseless hoodlum of sight ". The main source of visual impairment worldwide beside Diabetic Retinopathy is Glaucoma. It is discernible by augmented pressure inside the eyeball result in optic disc harm and moderate however beyond any doubt loss of vision. As the renaissance of the worsened optic nerve filaments isn't suitable medicinally, glaucoma regularly goes covered up in its patients anticipating later stages. All around it is assessed that roughly 60.5 million individuals beyond 40 years old experience glaucoma in 2010. This number potentially will lift to 80 million by 2020. Late innovation in medical imaging provides effective quantitative imaging alternatives for the identification and supervision of glaucoma. Glaucoma order can be competently done utilizing surface highlights. The wavelet channels utilized as a part of this paper are daubechies, symlet3 which will expand the precision and execution of classification of glaucomatous pictures. These channels are inspected by utilizing a standard 2-D Discrete Wavelet Transform (DWT) which is utilized to separate features and examine changes. The separated features are sustained into the feed forward neural system classifier that classifies the normal images and abnormal glaucomatous images.