Cervical cancer is one of the top reasons why women die from cancer. With early discovery and treatment, the challenges associated with this cancer may be minimized. The main goal of this research paper is to identify and classify cervical cancer using the Fisher Score-Convolutional Neural Network (FS-CNN). The data is collected from open-source database and then the unwanted noise in images were removed using central filter. This research is carried out to prevent problems occurring due to late diagnosis by ensuring early diagnosis and classification of cervical cancer. In order to classify the normal and abnormal cells as well as the types of abnormal cells, we enhance the weight of the Fisher Score- Convolutional Neural Network (FS-CNN) algorithm. With the aid of Fisher score, the proposed classification approach is chosen in order to enhance the use of CNN design parameters. The research confirms that deep learning techniques are helpful in early detection and classification of cervical cancer images. Accuracy metrics are employed to check performance and image quality assessments.

Classification, feature selection, prediction, the Fisher Score - Convolutional Neural Network (FS-CNN), Cervical cancer