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.