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Figure 2. ML model architecture design and optimization strategies. (A) Schematic workflow of the computational framework for training and evaluating various ML models; (B) Performance comparison of different classification models under three data augmentation strategies; (C) Evaluation of classification accuracy and loss function trends across ResNet architectures with varying depths and multi-level feature freezing strategies. ML: Machine learning; CNN: convolutional neural network; SVM: support vector machine; KNN: k-nearest neighbor; RT-GeomAug: real-time geometric augmentation; DBC-Aug: dynamic balance cropping augmentation; OS-Aug: over-sampling augmentation.