Abstract
The rapid and extensive growth in medical imaging modalities and their applications is creating a pressing need for computers and computing in image processing, visualization, archival, and analysis. In this article, a Matlab-based graphical user interface (GUI) program is proposed for the monitoring and early detection of keratoconus. The findings show the efficiency of the proposed to detect the early stage of keratoconus. The proposed neural network model produces accuracy, ranging from 96% to 92%. It considers, respectively, 2 classes (normal cornea and keratoconus) and 3 classes (keratoconus, suspected keratoconus or normal) which will increase to 99% with respect to the 2 classes of keratoconus and 94% to the 3 classes when combining topography parameters with OCT image corneal pachymetry measurements and clinical judgments. 
 The compatibility of the graphical interface components with common medical data and image analysis tools facilitates the involvement of the ophthalmologist in the digitization of the medical records, the image processing and the conception of multimodal artificial intelligence applications for medical imaging.
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