An Automated Identification of Cervical Cancer disease using Convolutional Neural Network Model

The cost of preventative measures is often lower than that of medical care in most nations. Early diagnosis of disease yields better treatment outcomes than late diagnosis. Unless we have a better idea of how to treat people, whatever help we can provide them would be appreciated. Among these illnesses is cervical cancer, which ranks number four on the list of the most prevalent cancers in women worldwide. Age and the usage of hormonal contraceptives are only two of the numerous variables that raise the risk of cervical cancer. Increased survival and lower mortality rates are the result of cervical cancer screenings that discover the disease at an early stage. The goal of this work is to apply machine learning methods to identify a model that can detect cervical cancer with high specificity and accuracy. Predictions of cervical cancer are made using a CNN model in this study. The Kaggle dataset of risk factors for cervical cancer, including 32 risk factors and 4 goal variables. Lastly, we compared our findings to those of other research and discovered that, based on various assessment metrics, our models performed better than those of the other studies in diagnosing cervical cancer.