A Comprehensive Analysis of COVID-19 Detection Using Bangladeshi Data and Explainable AI

High accuracy doesn't guarantee trustworthiness: While the VGG19 model achieved 98% accuracy in detecting COVID-19 from chest X-rays, the paper emphasizes that accuracy alone is insufficient—the researchers used LIME (an explainability tool) to show why the model made each decision, revealing which image regions actually influenced classifications.

Explainable AI is essential for medical applications: The study demonstrates that understanding model reasoning is critical in healthcare contexts; by examining both correct and incorrect classifications through XAI methods, clinicians can identify whether the model is relying on clinically relevant features or potentially misleading patterns.

Addressing data imbalances matters for fairness: The researchers applied SMOTE (a technique to balance underrepresented classes) to handle unequal numbers of different diagnosis types in their dataset, highlighting how imbalanced training data can lead to biased models that perform better on common cases than rare ones.

Local context improves model development: Using a Bangladeshi dataset of 4,350 images rather than only international datasets allowed the model to be tested on locally relevant populations, suggesting that responsible AI development requires diverse, geographically representative data to ensure models work equitably across different regions.

Transparency enables clinical validation: By using LIME to show which chest X-ray regions the model examined, the paper demonstrates how explainability allows medical professionals to validate whether AI systems are making decisions based on sound clinical reasoning rather than spurious correlations.