InuAid Chest X-rays AI Product: Respiratory diseases detection using Artificial Intelligence to significantly improve productivity and quality of diagnosis

ABSTRACT

The spread of Covid-19 virus around the world has taken many lives, quarantined people and shattered many industries. Due to high transmissibility of the virus and its silent incubation period in human beings, detection of the virus plays an important role to control its spread and to plan diagnostic and preventive measures. Laboratory tests such as Polymerase Chain Reaction (PCR) take more time and hence there is a need for rapid and accurate diagnostic methods to detect the virus to prevent its spread and combat it. Today PCR tests were used for diagnosing purposes and the chest x-ray was only used as the follow up of patients, hence these studies on the chest x-rays of patients with Covid-19 pneumonia or any other disease are still limited to the literature and must be improved in the future. In this project, the goal is to build an application for healthcare workers to monitor the health of lungs using the chest x-ray images of patients. The algorithm must be very accurate because it deals with the lives of people. Here we used computer vision and deep learning techniques in this project. The focus is to classify chest x-ray images and segment the abnormal region and to get more insights on the images from the available datasets. The diagnostic accuracy is the challenging part and to increase the detection efficiency due to the limited open-source data available. The data was collected from the internet. On classification, the trained model was able to achieve 93.10% accuracy and F1 Score of 0.93 after using transfer learning technique with pneumonia images. On segmentation, the Intersection Over Union value was found to be 0.91 on the validation data. © 2021 ACM.