Abstract: Keratoconus (KTC) is a noninflammatory disorder characterized by progressive thinning, corneal deformation, and scarring of the cornea. The pathological mechanisms of this condition have been investigated for a long time. In recent years, this disease has come to the attention of many research centers because the number of people diagnosed with keratoconus is on the rise. In this context, solutions that facilitate both the diagnostic and treatment options are quickly needed. The main contribution of this work is the implementation of an algorithm that is able to determine whether an eye is affected or not by keratoconus. The Kerato Detect algorithm analyzes the corneal tomography of the eye using Image Processing which is able to extract and learn the features of a keratoconus eye. The Convolution Neural Network (CNN) is used for the classification of keratoconus eye and normal eye. The results show that the Kerato Detect algorithm ensures a high level of performance, obtaining an accuracy of 91.38% on the data set. Kerato Detect can assist the ophthalmologist in rapid screening of its patients, thus reducing diagnostic errors and facilitating treatment.

Keywords: Convolution Neural Network (CNN), Keratoconus (KTC) .

| DOI: 10.17148/IARJSET.2021.86134