Background and Aim: Tuberculosis (TB), caused by Mycobacterium tuberculosis, continues to present a major health challenge, particularly in low- and middle-income countries with constrained diagnostic resources. Although chest radiography remains a standard screening method, the shortage of trained radiologists and variability in image interpretation hinder early and reliable detection. This study aimed to develop a lightweight convolutional neural network (CNN), trained from scratch with Swish activation, for accurate and efficient TB detection from chest radiographs. Patients and Methods: A heterogeneous dataset was created by aggregating multiple publicly available repositories, including Montgomery, Shenzhen, TB Base, and TBX11K. Images were standardized through grayscale conversion, resizing, and normalization, and subsequently partitioned into stratified training, validation, and test sets. The proposed CNN consisted of multiple convolutional layers with Swish activation and max pooling, followed by dense layers with dropout regularization. Model optimization was performed using the Adam optimizer, and performance was evaluated using accuracy, precision, recall, and F1-score metrics. External validation was conducted using unseen and independent datasets to assess generalizability. Results: The model achieved an overall accuracy of 92%, with precision and recall values exceeding 0.90 and an F1-score of 0.92. External validation confirmed the model?s generalizability, with inference times averaging 30-40 ms (specifically utilizing the Intel i5 CPU for inference) per image on consumer-grade hardware, demonstrating computational efficiency. Conclusions: The findings demonstrate that a lightweight, scratch-trained CNN can provide accurate and computationally efficient TB detection from chest radiographs. The deliberate choice of dataset heterogeneity and lightweight architecture allows strong diagnostic performance without reliance on high-end hardware, making the system suitable for TB screening programs in resourcelimited healthcare environments.  Kulcsszavak: tuberculosis detection, chest X-ray analysis, lightweight convolutional neural network, deep learning in medical imaging, low-resource healthcare deployment