A Novel Deep Learning Framework for the Detection of Tuberculosis using Chest X-ray Images - Shastri, Sourabh; Kumar, Sachin; Kumar, Sachin; Mansotra, Vibhakar (International Journal of Computer Sciences and Engineering, 2024)
Machine learning can play an important role in changing the dynamics of the modern healthcare system. In terms of the diagnosis field, Machine learning algorithms have offered tremendous support to Radiologists, healthcare workers, and other decision-makers. Early diagnosis of TB can stop the further spread and eventually mortality rate due to TB will fall. Currently, the standard method that is used for the diagnosis of TB takes one to four weeks while the rapid test takes 24 hours, so using Radiological images has an advantage over the existing standard method. In this paper, we have proposed a Novel Framework based on the application of Deep Learning to detect Tuberculosis (TB) using Chest X-ray images. In this work, 4200 images have been used to train the deep learning model. The model has achieved an accuracy of 99.41% in classifying Normal Chest X-rays and Tuberculosis (TB) Chest X-rays.