Which AI best reads chest X‑rays? A head-to-head test

Chest X‑rays remain a cornerstone of diagnosing lung conditions, but reading them reliably and quickly can be challenging. To explore how modern artificial intelligence could help, Harsh Santosh Salve and colleagues carried out a direct comparison of five leading convolutional neural network designs. Rather than developing a single model and calling it sufficient, the team put VGG19, ResNet50, InceptionV3, DenseNet121, and EfficientNetB0 through the same training and testing routine so differences would reflect model behavior rather than training quirks. The study focused on multi‑class classification, meaning the models had to distinguish among four possible labels for each image: Edema, Normal, Pneumonia, and Tuberculosis (TB). By holding the experimental conditions constant and initializing each architecture from the same pretrained weights, the researchers set out to produce a fair assessment that clinicians, hospital IT teams, and developers can use when choosing tools for chest radiograph workflows.

The team worked with a collection of Chest X‑ray images (CXR), using 6,092 images for training and 325 images for testing. Every architecture was initialized with ImageNet weights, augmented with a custom classifier layer, and then fine‑tuned under identical conditions so the comparison would be meaningful. The researchers measured overall accuracy and per‑class recall, and they also recorded practical considerations such as training time and model complexity. Results showed strong performance across the board, with VGG19 achieving the highest classification accuracy at 98.15% and ResNet50 close behind at 97.54%. In addition to raw accuracy, the study reports per‑class recall values to show how well each model detected specific categories like Tuberculosis (TB) versus Normal or Pneumonia. By testing five architectures—VGG19, ResNet50, InceptionV3, DenseNet121, and EfficientNetB0—under the same conditions, the work provides apples‑to‑apples data on both predictive skill and operational demands.

This empirical comparison supplies concrete evidence to guide choices about deploying deep learning on chest radiographs. Because the study evaluated not only accuracy but also per‑class recall, training time, and model complexity, it helps stakeholders balance clinical needs with computational and operational constraints. For example, an institution that prioritizes the absolute best accuracy might favor VGG19 based on these results, while another that needs a lighter model or faster training might weigh the tradeoffs differently. The work does not claim to replace clinicians, but it frames how different architectures perform on the same multi‑class CXR task and clarifies the tradeoffs involved in model selection. By presenting side‑by‑side results for VGG19, ResNet50, InceptionV3, DenseNet121, and EfficientNetB0, the study gives a practical evidence base for teams considering AI support for detecting Edema, Pneumonia, Tuberculosis (TB), or confirming Normal radiographs.