Stratum corneum nanotexture feature detection using deep learning and spatial analysis: a non-invasive tool for skin barrier assessment

This repository presents an automated approach for the data processing of Atomic Force Microscopy (AFM), enabling the construction of an extensive database for further academic investigation and visualization. The program seamlessly integrates critical steps, including the conversion of raw AFM data into PNG files, the utilization of computer vision techniques, and the implementation of state-of-the-art deep learning algorithms for accurate detection of circular nano objects (CNOs) and classification of various skin diseases. In addition, the algorithm incorporates the grid search method to determine the optimal hyperparameter settings, ensuring optimal performance and enhancing the reliability of the results.

Dependencies

Python 3.9+
matplotlib
numpy
opencv-python
scipy
scikit-image
ultralytics
customtkinter
scikit-learn
customtkinter

Directories

AD_Assessment_GUI.zip contains a cross-platform executable GUI, sample data, and a tutorial video.
Folder corneocyte dataset contains original corneocyte nanotexture images and annotated images for training AI models.
Folder models contains our fine-tuned YOLOv8-{N,S,M,L,X} and YOLOv9-{C,E} models.

Usage

Execution via cross-platform executable GUI
- Unzip AD_Assessment_GUI.zip
- Run AD_Assessment_GUI.exe
- Analysis results will be saved within the selected path in a folder titled CNO_Detection
Execution via python script
- Install packages in terminal:
```
pip install -r requirements.txt
```
- Run AD_Assessment_GUI.py
- Analysis results will be saved within the selected path in a folder titled CNO_Detection

Executable

Install PyInstaller in terminal:
```
pip install pyinstaller
```

Run command in terminal:

pyinstaller --onedir .\AD_Assessment_GUI.py

Contributions

[1] Liao, H-S., Wang, J-H., Raun, E., Nørgaard, L. O., Dons, F. E., & Hwu, E. E-T. (2022). Atopic Dermatitis Severity Assessment using High-Speed Dermal Atomic Force Microscope. Abstract from AFM BioMed Conference 2022, Nagoya-Okazaki, Japan.

[2] Pereda, J., Liao, H-S., Werner, C., Wang, J-H., Huang, K-Y., Raun, E., Nørgaard, L. O., Dons, F. E., & Hwu, E. E. T. (2022). Hacking Consumer Electronics for Biomedical Imaging. Abstract from 5th Global Conference on Biomedical Engineering & Annual Meeting of TSBME, Taipei, Taiwan, Province of China.

[3] Liao, H. S., Akhtar, I., Werner, C., Slipets, R., Pereda, J., Wang, J. H., Raun, E., Nørgaard, L. O., Dons, F. E., & Hwu, E. E. T. (2022). Open-source controller for low-cost and high-speed atomic force microscopy imaging of skin corneocyte nanotextures. HardwareX, 12, [e00341]. https://doi.org/10.1016/j.ohx.2022.e00341

Spaces:

jenhung
/

afm-analysis-web

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