Hyper-parameters optimized deep feature concatenated network for pediatric pneumonia detection

Abstract

Pneumonia, an infection that fills the alveoli of the lung region with pus causes a high rate of chronic illness and fatality amongst children across the globe. The most utilized imaging modality for pediatric pneumonia identification is chest X-rays, whose features are not always readily visible to the naked eye, making it challenging for radiologists to make precise predictions and save lives. Knowing how essential it is to have an early and distinct diagnosis of pneumonia, speeding up or automating the detection process is highly sensible. This article provides a smart, automated system that operates on chest X-ray images and can be successfully utilized for spotting pneumonia. The deep feature concatenation method used by this detection system intends to combine the outcomes of three effective pre-trained models to confirm the reliability of the suggested approach. To obtain its optimal performance, the hyper-parameters are demonstrated using a trial-and-error approach that surpasses existing models with 99.68% accuracy for the early diagnosis of pneumonia. A real-time data sample test is conducted on the proposed pneumonia detection model to evaluate its robustness.