Pelican Optimization with Majority Voting Ensemble Model for Tuberculosis Detection and Classification on Chest X-Ray Images

: Tuberculosis (TB) detection and classification on chest X-ray (CXR) images remains the most significant task in medical diagnosis. TB is a contagious disorder that affects the pulmonary region, and its diagnosing process often depends on CXR. CXR images are utilized for classifying and detecting TB lesions, including infiltrates, cavities, pleural effusions, and nodules. Manual analysis by radiologists includes a visual assessment of the X-ray images by a skilled physician or radiologist. There were many techniques to automatically classify and detect TB on CXR, including deep learning-based approaches, manual interpretation by radiologists, and computer-aided diagnosis (CAD) systems. This manuscript offers the design of pelican optimization with majority voting ensemble model for tuberculosis detection and classification (POMVE-TDC) technique on the CXR images. The core objective of the POMVE-TDC approach is to classify the incidence of TB on the CXR images. At the primary stage, the POMVE-TDC technique undergoes a contrast enhancement process. Besides, the densely connected network (DenseNet-161) model is applied for the extraction of feature vectors. Meanwhile, a pelican optimization algorithm (POA) based hyperparameter optimizer is designed for the DenseNet-161 model. Finally, a majority voting ensemble classifier comprising graph convolution network (GCN), autoencoder (AE), and extreme learning machine (ELM) models are used. The performance evaluation of the POMVE-TDC technique on the medical dataset highlights the significant outcomes with maximum accuracy of 98.83%