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
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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.
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