Synergistic Fusion of Ultrasound Image Augmentation, Ensemble Learning, and Transfer Learning for Robustness Against Overfitting in Machine Learning Model Technique

Abstract

                  Introduction: The frequency and increasing reliance on machine learning models in medical diagnostics calls for strategies to explain the accuracy of the results and validation methods. Overfitting continues to be a major hurdle, especially in instances of small and noise-ridden data like ultrasound images. When a model is too complex and fits the training set very well but performs poorly when presented with new data, it can lead to overfitting, which means we get stale, incorrect diagnostic predictions.

Objectives: This research proposes a solution to deal with the overfitting problem in machine learning-based ultrasound image analysis by combining three advanced methods: ultrasound image augmentation (using data doubling), ensemble learning, and transfer learning.

Methods: The ultrasound image augmentation is used as the concept behind it. Rotation, zoom, flip, and noise are applied to give the model more variation in training examples to generalize better. Pooling their predictions using ensemble learning mitigates the risk of relying on a single model's biases.

Results: This research evaluates the proposed model using a well-labeled dataset of ultrasound images under different conditions. Experiments show the amount of performance enhancement and generalization one can achieve in employing this synergistic fusion. The model retina Model can be seen to perform better compared to using individual retinal techniques with respect to accuracy, precision, recall, and F1 scores.

Conclusions: This was shown by a reduction in overfitting that occurred as we observed the model performance on validation data that was very close to training data. This study finds that a machine-learning model based on ultrasound image augmentation, ensemble learning, and transfer learning can improve the robustness of identifying liver diseases from ultrasound images.