Early Prediction and Diagnosis of Retinoblastoma Using Deep Learning Techniques

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 2.75D Convolutional Neural Network (CNN) to distinguish the foreground tumor cells from the background. The segmented tumor cells are classified and the malignancy of the tumor is classified into different stages and further grouped. The proposed optimization technique improves the algorithm’s parameter and suitable for multimodal images captured using a different configurations of disease under different circumstances. The suggested system improves the performance of the proposed approaches’ accuracy to 99.82%, sensitivity to 98.96%, and specificity to 99.32%. The proposed approach provides the best solution and an alternative approach for competitive methods.