Neural Network Approach For Classification And Detection Of Chest Infection

Abstract

Advances in computer technology have had a profound impact on our lives and the way we see the world. The healthcare industry is advancing thanks to the use of cutting-edge computer technology, which has transformed how numerous ailments are diagnosed and treated. The number of people suffering from chest-related illnesses is increasing at an alarming pace as a result of a wide range of conditions, including air pollution. Medical applications of image processing have emerged due to data collection tool development. It is now possible to make out the diagnosis through study of the features from medical investigation reports for a group of patients. That reduces the time and cost of the diagnosis, which may help plenty of people who are unable to access regular medical facilities due to intolerable cost. In this paper, automatic chest infection diagnosis is being diagnosed using a Neural Network. Two models are used, namely the Artificial Neural Network and the CNN Neural Network. The models are tested using NIH x-ray chest image data. Results are reported with 96.7% and 99.20% accuracy from the Artificial Neural Network and CNN, respectively.