Artificial Intelligence Based Hyper-parameter Adjustment on Deep Neural Networks: An Application of Detection and Classification of COVID-19 Diseases

Abstract

Earlier diagnosis of COVID-19 using radiological images become a challenging task in the
healthcare sector. The recent development of artificial intelligence (AI) methods find useful for the
investigation of radiological images to accomplish accurate COVID-19 diagnosis in an automated way. With
this motivation, this paper presents an AI based hyper-parameter adjustment on deep neural networks (AIHADNN)
technique for COVID-19 diagnosis and classification. The AIHA-DNN technique focuses on detecting
the existence of COVID-19 utilizing CXR (CXR) images. The AIHA-DNN technique involves the Weiner
filtering (WF) based pre-processing technique to get rid of the noise that exists in the Chest X-ray (CXR)
image. Then, the Squeeze Net based feature extractor is utilized to derive a useful set of feature vectors.
Moreover, improved grey wolf optimization based deep neural network (IGWO-DNN) technique is used for the
classification of CXR images into proper class labels. The IGWO algorithm is utilized for the hyper-parameter
adjustment of the DNN model to accomplish maximum detection rate. The performance validation of the
AIHA-DNN technique takes place on benchmark CXR datasets and the experimental values are examined with
respect to different measures. The simulation outcomes reported the supremacy of the AIHA-DNN technique
over the recent methods with the maximum sensitivity, specificity, and accuracy of 97.69%, 96.65%, and
97.61% respectively.