Deep learning model for discriminating early lung cancer from TB nodule and risk assessment management for lung nodule
In contrast to developed nations where the incidence of smoking is falling, cigarette smoking is on the rise in developing nations, many of which have a high rate of endemic tuberculosis.
In low and middle-income countries with both high rates of smoking and endemic tuberculosis, identification of early lung cancer can be significantly confounded by the presence of lung nodules due to latent TB (LTB). Unfortunately, these two entities cannot be readily distinguished, even by trained radiologists. This diagnostic equipoise leads to significant delays in cancer diagnosis, a disease for which timely intervention is paramount, with concomitant increases in lung cancer mortality.
We are going to develop a deep learning model which will detect and classify the presence of pulmonary nodules in CT scans and give a risk scoring base on the likelihood of malignant or benign. as well as distinguishing between TB and lung cancer.
Need to access the NLST data to further our ongoing research and development work in distinguishing between TB and lung cancer.
Development and testing of our deep learning model for pulmonary nodule detection and give a risk scoring and discriminating early lung cancer from TB nodule.
Munyanaik Kethavath, NITK,INDIA