An adaptive convolution neural network model for tuberculosis detection and diagnosis using semantic segmentation

The findings demonstrate the efficiency of our system in diagnosing TB from chest X-rays, potentially surpassing clinician-level precision. This underscores its effectiveness as a diagnostic tool, particularly in resourcelimited settings with restricted access to radiological expertise. Additionally, the modified Res-UNet model demonstrated superior performance compared to the standard U-Net, highlighting its potential for achieving greater diagnostic accuracy.