Lung diseases have remained at the heart of health predicants for over decades. Be it normal flue infections like Influenza virus or severe conditions like Pneumonia or COVID-19 chest x-rays play a crucial role in the diagnosis and treatment of all such diseases. X-rays are long known most inexpensive and convenient method of getting a picture of lung tissues and analyzing the extent to which they are affected or locating as any abnormality in them. However, interpreting such x-rays require highly professional and capable radiologists who might not be available easily in many areas. Moreover, accessing this massive number of patients’ data is undoubtedly a tedious task for doctors, and providing any automated assistance would be appreciated by them as well. In this regard, with the rapid spread of recent COVID-19, a lot of work is being done in this domain. Many people have developed deep learning tools for detecting different diseases from chest x-rays, but little work is done for disease localization in chest x-rays. A typical radiologist approach is to detect patches where abnormality occurs in a chest x-ray and on the basis of these findings, they make their diagnosis of whether a person is suffering from Pneumonia or COVID-19, or any other lung disease. Similarly, we have acquired annotations of ten different diseases on the Gulab-Devi Hospital chest x-rays dataset and plan to develop a deep-learning-inspired solution for disease localization in chest x-rays.