Comparison of Various Deep Learning Models Used to Detect and Classify Keratoconus Disease
Puja G. Ambalgekar1, Ashwini K B2
1Puja G. Ambalgekar, Department of Information Science and Engineering, Engineering R. V. College of Engineering Bengaluru (Karnataka), India.
2Ashwini K B, Department of Information Science and Engineering, Engineering R. V. College of Engineering Bengaluru (Karnataka), India.
Manuscript received on 10 October 2023 | Revised Manuscript received on 18 October 2023 | Manuscript Accepted on 15 November 2023 | Manuscript published on 30 November 2023 | PP: 1-5 | Volume-12 Issue-12, November 2023 | Retrieval Number: 100.1/ijitee.L974511121223 | DOI: 10.35940/ijitee.L9745.11121223
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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: The corneal condition keratoconus results in both corneal thinning and bulging, along with symptoms like astigmatism, light sensitivity, blurred vision, etc. Your eyes can be affected by genetic, environmental, and age-related problems because they are one of the most complex organs in the human body. From little discomfort to more serious vision problems that could harm your eyesight, this can happen. The screening for keratoconus requires a thorough examination of the cornea using various methods, including slit lamp analysis and corneal tomography. The goal of this study is to identify and categorise keratoconus using various machine-learning methods.
Keywords: Keratoconus Disease, InceptionV3 Xception, Mobile Net, Deep learning.
Scope of the Article: Deep learning