To enhance the V-BreathNet model's capability to detect a broader spectrum of lung abnormalities, particularly early-stage COVID-19 infections, several strategies can be implemented:
Data Augmentation: Increasing the diversity and quantity of the dataset by incorporating more early-stage COVID-19 x-ray images can help the model learn subtle patterns indicative of the disease at its onset.
Feature Engineering: Introducing additional features or engineered representations of the x-ray images that capture specific characteristics of early-stage COVID-19 infections can aid the model in making more accurate predictions.
Fine-tuning Layers: Fine-tuning specific layers of the V-BreathNet model to focus on early-stage COVID-19 indicators can improve its sensitivity to detecting subtle abnormalities associated with the disease.
Ensemble Learning: Implementing ensemble learning techniques by combining multiple versions of the V-BreathNet model trained on different subsets of data can enhance the model's overall performance and robustness in detecting various lung abnormalities, including early-stage COVID-19 infections.