Open Access

ARTICLE

Decision Support System for Diagnosis of Irregular Fovea

Ghulam Ali Mallah¹, Jamil Ahmed¹, Muhammad Irshad Nazeer^2,*, Mazhar Ali Dootio³, Hidayatullah Shaikh¹, Aadil Jameel¹
1 Shah Abdul Latif University, Khairpur, 77150, Pakistan
2 Sukkur IBA University, Sukkur, 7720, Pakistan
3 Shaheed Benazir University, Layari Karachi, 77202, Pakistan
* Corresponding Author: Muhammad Irshad Nazeer. Email:
(This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)

Computers, Materials & Continua 2022, 71(3), 5343-5353. https://doi.org/10.32604/cmc.2022.023581

Received 13 September 2021; Accepted 02 November 2021; Issue published 14 January 2022

Abstract

Detection of abnormalities in human eye is one of the well-established research areas of Machine Learning. Deep Learning techniques are widely used for the diagnosis of Retinal Diseases (RD). Fovea is one of the significant parts of retina which would be prevented before the involvement of Perforated Blood Vessels (PBV). Retinopathy Images (RI) contains sufficient information to classify structural changes incurred upon PBV but Macular Features (MF) and Fovea Features (FF) are very difficult to detect because features of MF and FF could be found with Similar Color Movements (SCM) with minor variations. This paper presents novel method for the diagnosis of Irregular Fovea (IF) to assist the doctors in diagnosis of irregular fovea. By considering all above problems this paper proposes a three-layer decision support system to explore the hindsight knowledge of RI and to solve the classification problem of IF. The first layer involves data preparation, the second layer builds the decision model to extract the hidden patterns of fundus images by using Deep Belief Neural Network (DBN) and the third layer visualizes the results by using confusion matrix. This paper contributes a data preparation algorithm for irregular fovea and a highest estimated classification accuracy measured about 96.90%.

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Keywords

Machine learning; deep belief neural network; eye disease; fovea

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