AI-Based Lanczos Filtering for Feature Selection and Structural Metrics Analysis, and Classification Method for Automated Detection of Major Conjunctivitis in Retinal Color Fundus Images
Madhusudhan S1, Anitha S2
1Prof. Madhusudhan S, Assistant Professor, Department of Electronics and Communication Engineering, Amruta Institute of Engineering and Management Sciences, VTU, Bengaluru (Karnataka), India.
2Dr. Anitha S, Professor, Department of Electronics and Communication Engineering, ACS College of Engineering, VTU, Bengaluru (Karnataka), India.
Manuscript received on 06 June 2026 | Revised Manuscript received on 11 June 2026 | Manuscript Accepted on 15 June 2026 | Manuscript published on 30 June 2026 | PP: 12-16 | Volume-15 Issue-5, June 2026 | Retrieval Number: 100.1/ijeat.F478915060826 | DOI: 10.35940/ijeat.F4789.15050626
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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: Various eye diseases appear differently in red, green, and blue channels of RGB colour models. Colour channels provide the primary information for detecting eye diseases. Selection and training of these channels are the primary tasks in pre-processing colour fundus images and the automatic detection of various eye diseases. Improvements in quality and appearance, as well as image enhancements, are performed during the pre processing stage without affecting the accuracy of fundus images. PSNR, MSE, DSSIM, FSIM, RMSE, UIQI, and SSIM are calculated to preserve structural information between the original image and colour-converted images. The test images are taken from the DRIVE fundus database and evaluated using colour-space structural models. The methods are tested using an OpenCV Python Jupyter notebook on a Windows platform with an Intel i5 processor at 3 GHz and 16 GB of RAM. The results are compared to determine the best colour space model for detecting cotton wool spots before post-processing.
Keywords: Cotton Wool Spot (CWS), PSNR, MSE, DSSIM, FSIM, RMSE, UIQI, and SSIM.
Scope of the Article: Computer Science and Engineering