ABOA-CNN: auction-based optimization algorithm with convolutional neural network for pulmonary disease prediction

Abstract

Nowadays, deep learning plays a vital role behind many of the emerging technologies. Few applications of deep learning include speech recognition, virtual assistant, healthcare, entertainment, and so on. In healthcare applications, deep learning can be used to predict diseases effectively. It is a type of computer model that learns in conducting classification tasks directly from text, sound, or images. It also provides better accuracy and sometimes outdoes human performance. We presented a novel approach that makes use of the deep learning method in our proposed work. The prediction of pulmonary disease can be performed with the aid of convolutional neural network (CNN) incorporated with auction-based optimization algorithm (ABOA) and DSC process. The traditional CNN ignores the dominant features from the X-ray images while performing the feature extraction process. This can be effectively circumvented by the adoption of ABOA, and the DSC is used to classify the pulmonary disease types such as fibrosis, pneumonia, cardiomegaly, and normal from the X-ray images. We have taken two datasets, namely the NIH Chest X-ray dataset and ChestX-ray8. The performances of the proposed approach are compared with deep learning-based state-of-art works such as BPD, DL, CSS-DL, and Grad-CAM. From the performance analyses, it is confirmed that the proposed approach effectively extracts the features from the X-ray images, and thus, the prediction of pulmonary diseases is more accurate than the state-of-art approaches.