Classification of Cervical Cell Images into Healthy or Cancer Using Convolution Neural Network and Linear Discriminant Analysis

Abstract

Cancer of the cervix is the disease that accounts for the majority of deaths in women. This disease accounts for nearly 12% of all cancers and has a high risk of death for women worldwide. If precancerous lesions are found early, the disease can be cured. Pap smear screening is known for its reliability and effectiveness in detecting cervical cell abnormalities early, but there is a risk of errors in manual image analysis. Using deep learning approaches in the domains of medicine and healthcare can be used for decision support systems to remove bias from observations. This paper presents a framework that utilizes deep learning and techniques to reduce the dimensions of features. The suggested framework captures deep features from a convolutional neural network (CNN) model and employs a feature reduction approach using linear discriminant analysis (LDA) to ensure computational cost reduction. The feature dimension derived from the CNN model produces a huge feature space that requires a feature reduction to eliminate redundant features. The features that have been reduced by linear discriminant analysis are used for the training of three classifiers, namely SVM, MLP, and K-NN, to generate final predictions. The evaluation of the proposed framework involved the utilization of three datasets that are openly accessible: the Herlev dataset, the Mendeley dataset, and the SIPaKMeD dataset, which achieved classification accuracies of 95.65% (SVM and MLP), 100% (MLP), and 97.54 (K-NN), respectively.