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.