Early Prediction and Diagnosis of Retinoblastoma Using Deep Learning Techniques
This work addresses early diagnosis of retinoblastoma for children, potentially saving lives and vision, but it appears incremental as it builds on existing deep learning techniques without specifying novel breakthroughs.
The researchers tackled the problem of early detection and diagnosis of retinoblastoma, a childhood eye cancer, by developing a deep learning system that uses preprocessing, segmentation, and classification on fundus images, achieving accurate prediction to prevent vision impairment.
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.