Classification of stages of maligancies using textron signatures of a cervical cyto image

Cervical cancer is one of the deadliest cancer known and is also a key research area in image processing. The main problem with this cancer is that it cannot be detected as it doesn't throw any symptoms until the final stages. This is attributed to the cancer itself and also to the lack of pathologists available to screen the cancer. Here we have proposed a novel approach to classify the various malignancies in cervical cyto images using the textural properties of the cervical cyto image. For grouping the stages of the cancer we have employed a decision based support system that would help classify the stages of the cancer and help the pathologist detect the cancer better. The proposed image has been tested with a set of images and has proved to be efficient.