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The study of using deep learning for detection of various thoracic diseases has been an active and challenging research area. Chest X-rays are currently the most common and globally used radiology practices for detecting thoracic diseases. Patients suffering from thoracic diseases need to take Chest X-Rays which are read by radiologists and a report is generated by them. However, today with the increase in the number of thoracic patients, a quick method to classify the disease and generate the report has become necessary. Also, patient history has to be considered for diagnosis. This paper offers a comparative study on the various deep learning techniques that can process chest x-rays and are capable of detecting the different thoracic diseases. Also, a technique has been proposed to classify 14 diseases namely Atelectasis, Cardiomegaly, Consolidation, Edema, Effusion, Emphysema, Fibrosis, Hernia, Infiltration, Mass, Nodule, Pneumonia, Pneumothorax, Pleural thickening based on the given X-rays using Residual Neural Network.