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Detection of Glaucoma Using HMM Segmentation and Random Forest Classification

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Inventive Systems and Control

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 436))

Abstract

Glaucoma is a retinal disease and the world's leading cause of blindness. Glaucoma is a single retinal condition that, like cataracts, has few symptoms which cause retinal damage and, as a result, a decrease in visual acuity. As a result, interpreting a 2D fundus picture is a complex undertaking. The course of treatment is critical in preventing patients from losing their vision. Immediately, a slew of studies demonstrated the discovery of retinal fundus images using various image processing techniques. In this research, we applied the Hidden Markov Model (HMM) picture segmentation on retinal pictures, then categorized using Random Forest, and achieved accuracy of 98.15%, sensitivity with 85.5%, and specificity of 92.9%.

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Author information

Authors and Affiliations

  1. Department of ECE, N.B.K.R. Institute of Science and Technology, S.P.S.R., Nellore, A.P., India

    Chevula Maheswari & K. Nagi Reddy

  2. J.N.T. University Ananthapuramu, Anantapur, A.P., India

    Gurukumar Lokku

Authors
  1. Chevula Maheswari
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  2. Gurukumar Lokku
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  3. K. Nagi Reddy
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Corresponding author

Correspondence to Gurukumar Lokku .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Design, Dayananda Sagar College of Engineering, Bengaluru, India

    V. Suma

  2. School of Information Technology, Deakin University, Geelong, VIC, Australia

    Zubair Baig

  3. Department of Mathematics and Computer Science, Ashland University, Wilmington, NC, USA

    Selvanayaki Kolandapalayam Shanmugam

  4. University of Haute Alsace, Alsace, France

    Pascal Lorenz

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Maheswari, C., Lokku, G., Nagi Reddy, K. (2022). Detection of Glaucoma Using HMM Segmentation and Random Forest Classification. In: Suma, V., Baig, Z., Kolandapalayam Shanmugam, S., Lorenz, P. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-19-1012-8_39

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