Tuberculosis (TB) is an infectious disease that claimed about 1.5 million lives in 2018. TB is most prevalent in developing regions. Even though TB disease is curable, it necessitates early detection to prevent its spread and casualties. Chest radiographs are one of the most reliable screening techniques; although, its accuracy is dependent on professional radiologists interpretation of the individual images. Consequently, we present a computer-aided detection system using a pre-trained convolutional neural network as features extractor and logistic regression classifier to automatically analyze the chest radiographs to provide a timely and accurate interpretation of multiple images. The chest radiographs were pre-processed before extracting distinctive features and then fed to the classifier to detect which image is infected. This work established the potential of implementing pre-trained convolutional neural network models in the medical domain to obtained good results despite limited datasets.