Computerized Detection of Limbal Stem Cell Deficiency from Digital Cornea Images

Limbal Stem Cell Deficiency (LSCD) is an eye disease that can cause corneal opacity and vascularization. In its advanced stage it lead to a degree of visual impairment. It involves the changing in semispherical shape cornea drooping downwards direction. LSCD hard be diagnosed at early stages. The color texture surface provide significant information about affected by LSCD. Parameters such as are very crucial differentiate normal from cornea. Although several medical approaches exist, most them requires complicated procedure devices. Therefore, this paper, we pursued development detection technique (LDT) utilizing image processing methods. Early diagnosis for physicians arrange effective treatment. proposed technique, developed method frontal images. A dataset 280 images lateral patients were used research. First, region both segmented, geometric features extracted through automated active contour model spline curve. While using feature selection algorithm. experimental results exhibited combined will exhibit accuracy 95.95%, sensitivity 97.91% specificity 94.05% with random forest classifier n = 40. As result, research stem cell deficiency system features’ fusion techniques digital eyes.