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• W4385560105 abstract "Human eye is the most important organ of the human body. As per the use of new technology like mobile phones and laptops, the human eye has become the organ which is mostly used nowadays. Though there are numerous eye diseases, keratoconus is a vision illness which causes a perfectly shaped round cornea to become thin and different in pointy shape. This conical shape prevents light entering the human eye and causes visual impairment or vision disorder. The symptoms of this disease differ from different series of phases. In recent years, the number of people diagnosed with this disease has increased. The disease can be cured with the cross-linking technique. The algorithm to detect keratoconus takes a number of parameters. These different parameters make it complicated to implement and test the algorithm to detect keratoconus. In this research paper, several theories are explained to detect keratoconus disease. They have used RGB image to HSV image approach, edge enhancement filters, and image smoothing techniques, these three-step preprocessing steps to qualify images. This method contains image enhancement using thresholding, slope-detection method, Canny filter, and Prewitt operator. Anterior and Lateral Segments’ Photographed Images can calculate geometric features such as eccentricity and solidity. One of the reviewed papers used backpropagation method in neural networks. There are some OCT image-based topography instruments to perform corneal topography, and some mathematical models are represented using Belin–Ambrosio curves. The feature selection technique using pattern deviation maps stands a different role. Anterior and posterior image data analysis is done by the researchers. This review is made up for better understanding of the previous research." @default.
• W4385560105 created "2023-08-04" @default.
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• W4385560105 date "2023-01-01" @default.
• W4385560105 modified "2023-10-16" @default.
• W4385560105 title "A Review for Detecting Keratoconus Using Different Techniques" @default.
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• W4385560105 doi "https://doi.org/10.1007/978-981-99-3250-4_35" @default.
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