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Related Experiment Video

Updated: Jan 19, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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Intelligent Identification for Rock-Mineral Microscopic Images Using Ensemble Machine Learning Algorithms.

Ye Zhang1, Mingchao Li2, Shuai Han3

  • 1State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China. jgzhangye@tju.edu.cn.

Sensors (Basel, Switzerland)
|September 14, 2019
PubMed
Summary

This study uses deep learning and Inception-v3 to identify rock minerals from microscopic images. Model stacking achieved 90.9% accuracy, improving upon individual machine learning models for efficient mineral identification.

Keywords:
CNNdeep learningmachine learningmodel stackingrock-mineral microscopic imagestransfer learning

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Area of Science:

  • Geological engineering
  • Mineralogy
  • Computer science

Background:

  • Microscopic mineral identification is crucial for geological engineering but is traditionally time-consuming and lab-intensive.
  • Deep learning and Convolutional Neural Networks (CNNs) offer efficient and intelligent solutions for analyzing microscopic mineral images.

Purpose of the Study:

  • To develop an automated system for identifying four key mineral types (K-feldspar, perthite, plagioclase, quartz) from microscopic images.
  • To compare the performance of various machine learning models and a stacked model for mineral identification.

Main Methods:

  • Utilized the Inception-v3 architecture for feature extraction from mineral microscopic images.
  • Implemented and evaluated six machine learning models: logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN), multilayer perceptron (MLP), and Gaussian Naive Bayes (GNB).
  • Employed 10-fold cross-validation for model evaluation and utilized model stacking with LR as the meta-classifier.

Main Results:

  • Individual models like LR, SVM, and MLP achieved approximately 90.0% accuracy.
  • The stacked model, combining LR, SVM, and MLP, demonstrated superior performance with an accuracy of 90.9%.
  • Model stacking was confirmed to effectively enhance the overall performance of mineral identification.

Conclusions:

  • Deep learning, specifically the Inception-v3 architecture, is effective for extracting features from mineral microscopic images.
  • Machine learning models, particularly LR, SVM, and MLP, show strong performance in high-dimensional feature analysis for mineral identification.
  • Model stacking is a valuable technique for improving the accuracy and robustness of automated mineral identification systems in geological engineering.