Classification of Cervical Cancer using Artificial Neural Network

Cervical cancer is developed in the cells which connect the vagina to the uterus. It can affect the circulatory system and the lymphatic system. This research aims to advance a method allows the efficient detection and classification of HPV stages using the pap smear image. Many studies have been done on images that classify cervical cancer based on its type. The stage of cancer can be classified using various features that have been extracted from the image. The study's goal was to establish the feature datasets that should be taken into account to formulate the proposed algorithms. The features included in this system were important to improve the classification of various systems. The features include the nucleus shape, diameter, and surface area, as well as its region, design and image resolution. This study focused on the various aspects of classifying cervical cancer. This paper describes an artificial neural network classifier that can predict cancer risk in women. This algorithm is different from other algorithms of machine learning. It uses less training datasets. The experiment finding shows 95 % accuracy when using an artificial neural network to classify whether the image is benign or malignant. The results of the study were presented and analyzed for further studies.