Cervical Cancer Detection Using Big Data Analytics and Their Comparative Analysis
V. Lakshmi Narasimhan, W. Tumisang Zaphaniah · 2023
The detection of cervix-based cancer cells relies heavily on artificial neural networks (ANNs). It is a huge challenge to detect cervical cancer because this cancer occurs with little or no observable symptoms. In order to quickly and precisely identify cervical cells, ANNs employ various architectures and strategies. This paper presents a systematic performance analysis of three deep learning algorithms, namely, backpropagation neural network (BPN), multilayer perceptron (MLP), and recurrent neural network (RNN) for predicting cervical cancer. It is concluded that the most appropriate technique to use for cervical cancer analysis is the multilayer perceptron (MLP) because it gives a higher accuracy of 90% when compared with BPN (60%) and RNN algorithms (88%). MLP is also faster in performance (70%) when compared with the RNN algorithm (0%).