The chest is a part of the human anatomy located between the neck and the abdomen. The chest has more priority in the human body. Detection of chest disease becomes a challenging factor faced by doctors. So, chestrelated diseases are the main reason for a huge number of death in the world over the last few decades. To reduce the large scale of deaths from chest diseases there is a need for a reliable, accurate, and feasible system that can detect such diseases in time for proper treatment. A large dataset of chest X-rays is available. So, this is a good condition to use this dataset and implement the project. The difficulty is an available large dataset has a lot of noise. So, Chest x-ray images will be pre-processed to extract their features and make them suitable for disease detection as normal or abnormal. We will use Machine Learning to process data as well as create models for diagnosing patients and propose an automatic chest disease detection system. In particular, we will demonstrate that the most commonly occurring thoracic diseases such as Cardiomegaly, Pneumonia, Pneumothorax, Mass, Nodule, Infiltration, and Effusion can be detected.



Software And Hardware