An Efficient System for Diagnosis of Human Blindness Using Image-Processing and Machine-Learning Methods

Abstract

The two main causes of blindness are diabetes and glaucoma. Routine diagnosis of blindness is based on the conventional robust mass-screening method. However, despite being cost-effective, this method has some problems as a human eye-disease detection method because there are many types of eye disease that are similar or that result in no visual changes in the eye image. These issues make it highly difficult to recognize blindness and control it. Moreover, the color of the macula of the spot can be very close to that of the affected macula in a variety of eye diseases, which suggests that the color of the macula spot can indicate various possibilities, rather than one. This paper discusses the shortcomings of current blindness-screening and monitoring systems and presents a feature-based blindness diagnosis approach using digital eye fundus images for the purpose of automated diagnosis of eye disorders, considering three conditions: healthy eye, diabetic retinopathy (DR), and glaucoma. As such, this paper develops a computer-aided diagnosis (CAD) method for automated detection of human blindness. The proposed approach integrates Gabor filter features, statistical features, colored features, morphological features, and local binary pattern features, then compares them with features drawn from a standard dataset of 1580 fundus images. Several classification techniques were applied to the extracted-features neural network (NN), support vector machine (SVM), naïve bias (NB). SVM classifiers show the most promising accuracy. They achieved 93.3% over the other classifiers.

A Robust Approach for Ulcer Classification/Detection in WCE Images

“…This process A Robust Approach for Ulcer Classification/Detection in WCE Images requires more effort and time, contributing to the exhaustion and monotony of the screening process. Contemporary scientific research has focused on the development of computer systems with the ability to autonomously analyze and interpret big medical images data [1,2,3]. These systems must cover all medical images acquisition modalities, such as X-rays, mammography, ultrasound, tomography, magnetic resonance imaging, cardiography and endoscopy.…”

Convolutional Neural Network for Segmentation and Classification of Glaucoma

“…For the final stage, a pre-trained transfer learning model, DenseNet-201, is used for feature extraction in conjunction with a deep convolutional neural network (CNN). The classification task utilizes a CNN approach, with the ultimate outcome indicating whether or not glaucoma is present in the diagnosis [7].…”