Glaucoma-Deep: Detection of Glaucoma Eye Disease on Retinal Fundus Images using Deep Learning

Abstract

Detection of glaucoma eye disease is still a challenging task for computer-aided diagnostics (CADx) systems. During eye screening process, the ophthalmologists measures the glaucoma by structure changes in optic disc (OD), loss of nerve fibres (LNF) and atrophy of the peripapillary region (APR). In retinal images, the automated CADx systems are developed to assess this eye disease through segmentation-based hand-crafted features. Therefore in this paper, the convolutional neural network (CNN) unsupervised architecture was used to extract the features through multilayer from raw pixel intensities. Afterwards, the deep-belief network (DBN) model was used to select the most discriminative deep features based on the annotated training dataset. At last, the final decision is performed by softmax linear classifier to differentiate between glaucoma and non-glaucoma retinal fundus image. This proposed system is known as Glaucoma-Deep and tested on 1200 retinal images obtained from publically and privately available datasets. To evaluate the performance of Glaucoma-Deep system, the sensitivity (SE), specificity (SP), accuracy (ACC), and precision (PRC) statistical measures were utilized. On average, the SE of 84.50%, SP of 98.01%, ACC of 99% and PRC of 84% values were achieved. Comparing to state-of-the-art systems, the Nodular-Deep system accomplished significant higher results. Consequently, the Glaucoma-Deep system can easily recognize the glaucoma eye disease to solve the problem of clinical experts during eye-screening process on large-scale environments.

Authors and Affiliations

Qaisar Abbas

Keywords

Related Articles

Performance Evaluation of Completed Local Ternary Pattern (CLTP) for Face Image Recognition

Feature extraction is the most important step that affects the recognition accuracy of face recognition. One of these features are the texture descriptors that are playing an important role as local features descriptor i...

Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions

Optimal path planning refers to find the collision free, shortest, and smooth route between start and goal positions. This task is essential in many robotic applications such as autonomous car, surveillance operations, a...

Mobile Arabchat: An Arabic Mobile-Based Conversational Agent

The conversation automation/simulation between a user and machine evolved during the last years. A number of research-based systems known as conversational agents has been developed to address this challenge. A conversat...

Impact of Heterogeneous Deployment on Source Initiated Reactive Approach

Selection of an optimal number of high energy level nodes and the most appropriate heterogeneity level is a prerequisite in the heterogeneous deployment of wireless sensor network, and it serves several purposes like enh...

Human Related-Health Actions Detection using Android Camera based on TensorFlow Object Detection API

A new method to detect human health-related actions (HHRA) from a video sequence using an Android camera. The Android platform works not only to capture video images through its camera, but also to detect emergency actio...

Download PDF file
  • EP ID EP259518
  • DOI 10.14569/IJACSA.2017.080606
  • Views 109
  • Downloads 0

How To Cite

Qaisar Abbas (2017). Glaucoma-Deep: Detection of Glaucoma Eye Disease on Retinal Fundus Images using Deep Learning. International Journal of Advanced Computer Science & Applications, 8(6), 41-45. https://europub.co.uk/articles/-A-259518