Efficient Machine Learning Techniques to Detect Glaucoma using Structure and Texture based Features
Nataraj Vijapur1, R. Srinivasa Rao Kunte2

1Nataraj Vijapur*, Electronics & Communication Department, KLE Dr. M.S. Sheshgiri College of Engg & Technology, Belagavi, Karnataka, India.
2Srinivasa Rao Kunte, Electronics & Communication Department, Sahyadri College of Engineering & Management, Mangalore, India.

Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 193-201 | Volume-9 Issue-2, July 2020. | Retrieval Number: B3374079220/2020©BEIESP | DOI: 10.35940/ijrte.B3374.079220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Survey of world health organization has revealed that retinal eye disease Glaucoma is the second leading cause for the blindness worldwide. It is the disease which will steal the vision of the patient without any warning or symptoms. About half of the world Glaucoma patients are estimated to be in Asia. Hence, for social and economic reasons, Glaucoma detection is necessary in preventing blindness and reducing the cost of surgical treatment of the disease. The objective of the paper is to predict and detect Glaucoma efficiently using image processing and machine learning based classification techniques. Segmentation techniques such as unique template approach, Gray Level Coherence Matrix based feature extraction approach and wavelet transform based approach are used to extract these structure and texture based features. Combination of structure based and texture based techniques along with machine learning techniques improves the efficiency of the system. Developed efficient Computer aided Glaucoma detection system classifies a fundus image as either Normal or Glaucomatous image based on the structural features of the fundus image such as Cup-to-Disc Ratio (CDR), Rim-to-Disc Ratio (RDR), Superior and Inferior neuro-retinal rim thicknesses, Vessel structure based features and Distribution of texture features in the fundus images.
Keywords: GLCM, Glaucoma, Cup-to-Disc Ratio (CDR), Rim-to-Disc Ratio (RDR), Image Processing, Machine Learning, Structure features and Texture features.