Robust Tuberculosis Detection using Optimal Deep Learning Model using Chest X-Rays

Accurate Tuberculosis (TB) screening using chest X-rays and artificial intelligence (AI) has the potential in increasing the quality of the healthcare services. Early detection of TB using automated tools find beneficial to decrease the severity level of the diseases. Therefore, the recent developments of the deep learning (DL) models are used in the design of automated TB detection tools. With this motivation, this article focuses on the design of new Harris Hawks optimization with Deep Learning Enabled Tuberculosis Classification (HHODL-TBC) model on chest X-rays. The proposed HHODL-TBC model focuses on the recognition and classification of TB effectually. It follows a three stage process: median filtering based noise removal, U-Net segmentation, MobileNetv2 feature extraction, HHO based hyperparameter tuning, and gated recurrent unit (GRU) classifier. The design of the HHO algorithm assist in the optimal hyperparameter selection of the GRU model. A comprehensive set of simulations were performed for illustrating the improvised results of the HHODL-TBC model, and the results demonstrate the improved outcomes of the HHODL-TBC model with higher accuracy of 99.33%.