Whale Optimization Algorithm with Fuzzy Wavelet Neural Network for Pneumonia Detection and Classification

Abstract

Pneumonia detection and classification are a vital medical imaging task that purposes to automatically recognize and classify pneumonia-related abnormalities in chest radiographs or Chest X-Ray (CXR) imaging. Accurate and prior identification of pneumonia is important for suitable treatment and recovering patient results. The main drive is to recognize whether an X-ray image indicates the presence of pneumonia or not. A binary classification techniuqe is trained to distinguish normal and pneumonia X-rays. Deep Learning (DL) approaches are revealed major success in automating this process, assisting healthcare specialists in analyzing pneumonia more effectively. This article presents a Whale Optimization Algorithm with Fuzzy Wavelet Neural Network for Pneumonia Detection and Classification (WOAFWNN-PDC) technique on CXRs. The purpose of the WOAFWNN-PDC technique is to apply optimal DL approaches for the recognition and classification of pneumonia. In the presented WOAFWNN-PDC technique, Gaussian Filtering (GF) approach is used for the noise removal process. In addition, the MobileNetv3 model is utilized for the feature extraction method. Moreover, the FWNN technique was applied to the classification of pneumonia. Finally, the WOA can be executed for an optimum selection of the parameters related to the FWNN approach. The simulation value of the WOAFWNN-PDC algorithm was assessed on a benchmark medical database. The comparative analysis exhibits better results than the WOAFWNN-PDC technique.