GNNs Python script for TCM fingerprints

We employed deep residual networks, specifically ResNet-18, ResNet-34, and ResNet-50 architectures, to analyze and categorize chromatographic fingerprints of traditional Chinese medicines (TCM).
1. Model Architecture:
ResNet Variants: Implemented ResNet-18, ResNet-34, and ResNet-50 to manage different complexities in image data.
Pre-trained Weights: Initialized with weights from the ImageNet dataset to improve generalization across medical images.
Custom Top Layer: Included a 50% dropout rate and a fully connected output layer tailored for classifying chromatographic fingerprints.
2. Data Preprocessing and Augmentation:
Standardization: Resized all images to 384x384 pixels and normalized using standard values.
Augmentation: Applied random flips, rotations, color jittering, and affine transformations to increase model robustness.
3. Training Setup:
Loss Function: Utilized cross-entropy loss for effective training.
Optimizer: Used Adam optimizer with initial learning rate of 0.0001 and weight decay.
Early Stopping: Implemented to halt training when no improvement in validation loss is observed, preventing overfitting.
4. Validation and Performance Metrics:
Dataset Split: Data divided into training (70%), validation (15%), and testing (15%) sets.
Metrics: Monitored accuracy, recall, F1 score, and precision to evaluate model performance.
5. Practical Application:
Image Prediction Workflow: Developed a workflow for loading trained models, preprocessing input images, and classifying new images, facilitating practical use in diagnostics.