According to a startling WHO research from 2018, tuberculosis (TB) takes the lives of 1.5 million people annually throughout the world and affects 10 million more. Sadly, more than 4000 individuals lose their lives to tuberculosis every day, making new diagnostic techniques critically necessary. A large number of these deaths could have been avoided if the illness had been discovered earlier. Recent literature shows that deep learning (DL) approaches have potential in automating tuberculosis detection through medical image analysis. Even though DL has shown promise in a few areas, there are few thorough studies on TB diagnosis. For DL to be effective, a significant number of high-quality training samples are necessary, and this challenge is compounded by the poor contrast that is frequently present in TB Chest X-ray (CXR) images. This research evaluates the outcome of image augmentation on deep learning performance to address this important issue. The concept augmentation design successfully climaxes fault-finding items and common or local traits in TB CXR concepts. The Contrast Limited Adaptive Histogram Equalization (CLAHE), High-Frequency Emphasis Filtering (HEF), and Unsharp Masking (UM) methods were the three particular approaches judged. For transfer knowledge, enhanced concept samples were then augmenting into pre-prepared ResNet and EfficientNet models. Interestingly, our procedure realized a 94.8% Area Under the Curve (AUC) score and an extraordinary 89.92% categorization accuracy on a TB countenance dataset culled from the candidly free Shenzhen dataset. These verdicts climax the value of concept augmentation in reconstructing DL’s TB demonstrative efficiencies and manifest the potential for more precise and prompt discovery concerning this inevitable sickness.