Computer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm

Computer-aided diagnosis (CAD) models exploit artificial intelligence (AI) for chest X-ray (CXR) examination to identify the presence of tuberculosis (TB) and can improve feasibility performance CXR TB screening triage. At same time, interpretation is a time-consuming subjective process. Furthermore, high resemblance among radiological patterns other lung diseases result in misdiagnosis. Therefore, computer-aided using machine learning (ML) deep (DL) be designed accurately. With this motivation, article develops Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification (WSODTL-TBC) model on Chest X-rays (CXR). The presented WSODTL-TBC aims detect classify images. Primarily, undergoes image filtering techniques discard noise content U-Net-based segmentation. Besides, pre-trained residual network two-dimensional convolutional neural (2D-CNN) applied extract feature vectors. In addition, WSO algorithm long short-term memory (LSTM) was employed identifying classifying TB, where as hyperparameter optimizer LSTM methodology, showing novelty work. validation carried out benchmark dataset, outcomes were investigated many aspects. experimental development pointed betterment over existing algorithms.