An Expert System to Predict Eye Disorder Using_x000D_ Deep Convolutional Neural Network

Abstract

Glaucoma according to the W.H.O is one of the major causes of blindness worldwide. Due to its complexity and silent nature_x000D_ early detection of this disease makes it hard to detect. There have been several techniques over the years for classification which_x000D_ have shown significant improvement over the past decade or two. Some of the many classification models are SVM (support_x000D_ vector machine), KNN (K- Nearest Neighbors), Decision tree, Logistic Regression and ANN (Artificial Neural Network) back_x000D_ propagation. For this paper we would consider different procedure and method of early detection of the glaucoma disease using_x000D_ the MATLAB Deep Convolutional Neural Network (DCNN). The DCNN based expert system basically works like the human_x000D_ brain with input, neurons, hidden layers and output. For this project Fundus image of both healthy image and glaucoma image_x000D_ are collected with good lighting condition so that all hidden features can be identify. The Fundus image are then passed through_x000D_ different image processing method such as Grayscale, B&W, Complement, Robert, Resize and power Transform. The fundus is_x000D_ then passed through a texture feature extraction algorithm know as Deep Convolutional Neural Network (DCNN). The features_x000D_ gotten are Contrast, Correlation, energy, Homogeneity, Entropy, Mean, Standard deviation, Variance, skewness and Kurtosis._x000D_ After the feature extraction the data are arrangement on a spreadsheet which serves as a means of record. Lastly, a deep_x000D_ convolutional neural network is written with one hidden layer, 16 input neuron and 2 output either healthy or not. The data are_x000D_ split into train and test dataset with 70% for training 15% validation and 15% for testing. Accuracy of detection was 92.78%_x000D_ with the execution time of 5.33s only depending on the number of iteration or epochs.