Tmrf: Trustworthy microstructure recognition framework with deep learning and explainable AI

This study introduces the Trustworthy Microstructural Recognition Framework (TMRF), aimed at enhancing microstructural recognition with a focus on explainability, human-computer interaction, and accountability. Evaluating microstructural images across seven classes, the framework employed transfer learning models in two experiments: Group 1 (three distinct classes) and Group 2 (all classes). DenseNet outperformed other models, achieving 97%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} accuracy and 96%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} F1 scores. Explainable AI (XAI) improved interpretability, with Occlusion and SmoothGrad providing insertion fidelity scores of 60%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} and 50%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} for Group 1 and Group 2, respectively. Visualization highlighted the model's detailed understanding of Group 1's microstructures. Practical demonstration over the Time-Temperature-Transformation (TTT) diagram showcased Group 1's impeccable micrograph identification. The TMRF Fact Sheet, emphasizing explainability, underscores the framework's role in fostering trust in microstructural recognition systems.