<![CDATA[Tuberculosis is an infectious disease that primarily affects the lungs and remains a major health concern due to the difficulty of diagnosis through manual interpretation of chest X-ray images. This study aims to develop an automatic tuberculosis classification system using the Backpropagation Neural Network (BPNN) method to improve diagnostic accuracy. The dataset used in this study was obtained from the Kaggle Tuberculosis (TB) Chest X-ray Dataset, consisting of 7.000 images divided into two classes normal and tuberculosis. The research stages include image preprocessing such as conversion to grayscale, resizing to 256×256 pixels, contrast enhancement using histogram equalization, and noise reduction using a median filter. Experiments were conducted by varying the number of hidden layers 2, 3, and 4 to analyze the effect of network architecture complexity on classification performance. The results showed that the configuration with 2 hidden layers and [100 50] neurons achieved the best performance with an accuracy of 93.57%. The findings indicate that deeper network architectures do not always guarantee higher accuracy and may increase computational load. Overall, this configuration provides an optimal balance between learning capability and accuracy, demonstrating the potential of the BPNN method in supporting early and reliable tuberculosis detection through machine learning based chest X-ray image analysis for clinical decision support.]]>
