Deep Learning Classification to Improve Diagnosis of Cervical Cancer Through Swarm Intelligence-Based Feature Selection Approach

Abstract

Cervical cancer is one of the predominant cancers that cause death in women globally. This disease progresses slowly and is a curable cancer, if detected well in advance. Various studies on different data mining models used for diagnosing this disease have been carried out. Different approaches including support vector machine and recursive feature elimination (SVM-RFE) and support vector machine and principal component analysis (SVM-PCA) had been designed for cervical cancer diagnosis. However, these pose various challenges including low accuracy and high processing time for classification. The proposed system addresses these issues by designing a long short-term memory with artificial bee colony (LSTM-ABC) algorithm for cervical cancer detection. The study takes the cervical cancer dataset as an input and uses synthetic minority oversampling technique (SMOTE) for solving the class imbalance issue. From the preprocessed data, the feature selection is performed using artificial bee colony (ABC) algorithm. Long short-term memory (LSTM) scheme is then employed for classifying cervical cancer based on the selected features. Experimental outcomes demonstrate that the proposed system delivers superior results than the previous works with respect to sensitivity, accuracy, and specificity.