Abstract
In recent years, more patients were detected with corneal abnormalities. It is critical to perform diagnosis of clinical or subclinical Keratoconus accurately. The main diagnostic tool for Keratoconus is corneal topography; leading to more Laser refractive surgeries. In this paper, we recommend a novel approach which can detect Keratoconus more efficiently. The parameters derived for feature extraction are corneal volume, corneal thickness, thinnest corneal thickness, corneal area, corneal perimeter. Hybrid feed forward network along with Naive Bayes classifier is used for classification. This proposed method detects and classifies Keratoconus and the performance metrics are also evaluated using MATLAB-based simulations and finally the performance of proposed method is compared with some existing techniques.
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