Developing an Automatic Asbestos Detection Method Based on a Convolutional Neural Network and Support Vector Machine

Abstract

When buildings containing asbestos are demolished, fine asbestos fibers are released, which can result in serious adverse health effects. Therefore, leakage is monitored to prevent the dispersion of asbestos fibers. Airborne asbestos fibers are monitored via microscopic observation, which requires significant manual labor. In this study, we developed a machine-learning model to automatically detect asbestos fibers in phase-contrast microscopy images. The model was based on a pre-trained convolutional neural network as its foundation, with fully connected layers and a support vector machine (SVM) serving as classifiers. The effects of fine-tuning, class weighting, and hyperparameters were assessed to improve model performance. Consequently, the SVM was chosen as a classifier to improve overall model performance. In addition, fine-tuning improved the performance of the models. The optimized detection model exhibited high classification performance with an F1 score of 0.83. The findings of this study provide valuable insights into effectively detecting asbestos fibers.

Keywords

• airborne asbestos fibers
• machine learning
• fine-tuning
• phase-contrast microscopy
• mesothelioma