results_en.md

Conclusion

First and foremost, it is important to emphasize that the results obtained can be attributed to a series of factors, including the architecture of neural networks, the number of layers, the complexity of the models, the size of the dataset, the preprocessing and data augmentation techniques used.

Challenges in Generalizing to Real-World Data:

The models' performance significantly declined when evaluated on real-world data, suggesting a generalization difficulty. Possible reasons include variations in image acquisition conditions, differences in image quality, image artifacts not found in the training and validation sets, and variations in patient position and condition.

Potential Improvement Strategies:

Dataset Expansion:

• Collecting more X-ray images under varied conditions may help models learn a more robust representation of patterns associated with Covid-19 and pneumonia.
• Capturing more examples from different perspectives, lighting conditions, and image artifacts present in real clinical environments can help improve the models' generalization.

More Sophisticated Augmentation:

• Experimenting with more sophisticated data augmentation techniques, such as simulations of distortions or more complex variations, may help models adapt to a wider range of image variations.

Hyperparameter Tuning:

• Testing different values for regularization parameters, such as the dropout rate, may help reduce overfitting in models.
• Testing new learning rates and batch sizes during training may also help find a configuration that improves model generalization.

Ongoing Evaluation:

• Continuously refining model performance on real-world data can adjust models to more specific situations.

Specific Preprocessing Techniques

The study “Melhoria de Imagens de Raio X Para Detecção de Pneumonia por Covid-19” conducted by Aylla Christinne Feitosa Rodriges (IFGoiano) demonstrates that exists specific preprocessing techniques that can enhance disease characteristics. This paves the way for advancements in disease detection, providing sharper and more detailed images.

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In conclusion, improving the models' ability to generalize results to real-world data requires a combination of enhancing datasets, more advanced data augmentation techniques, and careful adjustments of hyperparameters. Additionally, collaboration and evaluation with medical and clinical experts are ideal to understand the nuances and specificities of X-ray images and the characteristics of each illness.