Disease detection using chest X-ray (CXR) images is one of the most popular radiology methods to diagnose diseases through a visual inspection of abnormal symptoms in the lung region. A wide variety of diseases such as pneumonia, heart failure and lung cancer can be detected using CXRs. Although CXRs can show the symptoms of a variety of diseases, detecting and manually classifying those diseases can be difficult and time-consuming adding to clinicians' work burden. Research shows that nearly 90% of mistakes made in a lung cancer diagnosis involved chest radiography. A variety of algorithms and computer-assisted diagnosis tools (CAD) were proposed to assist radiologists in the interpretation of medical images to reduce diagnosis errors. In this work, we propose a deep learning approach to screen multiple diseases using more than 220,000 images from the CheXpert dataset. The proposed binary relevance approach using Deep Convolutional Neural Networks (CNNs) achieves high performance results and outperforms past published work in this area.Clinical relevance- This application can be used to support physicians ans speed-up the diagnosis work. The proposed CAD can increase the confidence in the diagnosis or suggest a second opinion. The CAD can also be used in emergency situations when a radiologist is not available immediately.