Retinal and cancer cell image segmentation for predicting the diseased images

Image segmentation in conventional learning approaches, the consumer applies only labeled or unlabelled training data set. The advanced application of segmentation in semi supervised learning to build better understanding of learners such a way that user could able to use both labeled data and un labeled data. In this research work focus to multi image model for semi supervised segmentation in retina and cancer cell images. The principal assets of the paper are that predicting the diseases diabetic and cancer with efficient training mechanism in a way that less human endeavor and higher correctness are achieved. We highlight the semi supervised segmentation in multi image model to classify diseased image or non diseased image.