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28 March 2023 SVM-based classification on AFM images of prostate cancer cells
Jianzhong Yu, Hanxing Gao, Xiaoxia Si, Hongqin Yang, Yuhua Wang
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Proceedings Volume 12601, SPIE-CLP Conference on Advanced Photonics 2022; 1260106 (2023) https://doi.org/10.1117/12.2667179
Event: SPIE-CLP Conference on Advanced Photonics 2022, 2022, Online Only
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.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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Jianzhong Yu, Hanxing Gao, Xiaoxia Si, Hongqin Yang, and Yuhua Wang "SVM-based classification on AFM images of prostate cancer cells", Proc. SPIE 12601, SPIE-CLP Conference on Advanced Photonics 2022, 1260106 (28 March 2023); https://doi.org/10.1117/12.2667179
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KEYWORDS
Prostate cancer
Feature selection
Machine learning
Atomic force microscopy
Cancer
Data modeling
Prostate
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