  Retinoblastoma is the most prominent childhood primary intraocular malignancy
that impacts the vision of children and adults worldwide. In contrasting and
comparing with adults it is uveal melanoma. It is an aggressive tumor that can
fill and destroy the eye and the surrounding structures. Therefore early
detection of retinoblastoma in childhood is the key. The major impact of the
research is to identify the tumor cells in the retina. Also is to find out the
stages of the tumor and its corresponding group. The proposed systems assist
the ophthalmologists for accurate prediction and diagnosis of retinoblastoma
cancer disease at the earliest. The contribution of the proposed approach is to
save the life of infants and the grown-up children from vision impairment. The
proposed methodology consists of three phases namely, preprocessing,
segmentation, and classification. Initially, the fundus images are preprocessed
using the Liner Predictive Decision based Median Filter (LPDMF). It removes the
noise introduced in the image due to illumination while capturing or scanning
the eye of the patients. The preprocessed images are segmented using the
Convolutional Neural Network (CNN) to distinguish the foreground tumor cells
from the background.
