SVM-based classification on AFM images of prostate cancer cells

Abstract

Prostate cancer is the 2nd most commonly occurring male cancer and the 4th most common cancer overall. Early detection and diagnosis are important for clinical treatment. Atomic force microscopy (AFM)-based techniques have been shown to have potential in detecting malignant cancers and artificial intelligence can improve the accuracy of diagnostic and prognostic prediction tests. In this study, the classification of AFM images of prostate cells was performed using machine learning. For early prediction, we used the support vector machine (SVM) to classification prostate cells and compare the classification performance with the remaining four conventional classifiers such as logistic regression (LR), stochastic gradient descent (SGD), K-nearest neighbours (KNN), random forest (RF). Most of the classifiers did well after using the feature selection method (BorutaShap). The results show that the accuracy (ACC) of the features selected using the BorutaShap algorithm combined with the SVM classifier can reach 82.5%. Our current study demonstrates that AFM imaging combined with machine learning can be used to identify prostate cancer cells with an effective classification performance and robustness.