A Lightweight Deep Learning-Based Pneumonia Detection Approach for Energy-Efficient Medical Systems

Bandar Almaslukh,Arun K Sangaiah

doi:10.1155/2021/5556635

Bandar Almaslukh, Arun K Sangaiah

Open Access

https://doi.org/10.1155/2021/5556635

Copy DOI

Abstract

Early detection of pneumonia disease can increase the survival rate of lung patients. Chest X-ray (CXR) images are the primarily means of detecting and diagnosing pneumonia. Detecting pneumonia from CXR images by a trained radiologist is a challenging task. It needs an automatic computer-aided diagnostic system to improve the accuracy of diagnosis. Developing a lightweight automatic pneumonia detection approach for energy-efficient medical systems plays an important role in improving the quality of healthcare with reduced costs and speedier response. Recent works have proposed to develop automated detection models using deep learning (DL) methods. However, the efficiency and effectiveness of these models need to be improved because they depend on the values of the models’ hyperparameters. Choosing suitable hyperparameter values is a critical task for constructing a lightweight and accurate model. In this paper, a lightweight DL approach is proposed using a pretrained DenseNet-121-based feature extraction method and a deep neural network- (DNN-) based method with a random search fine-tuning technique. The DenseNet-121 model is selected due to its ability to provide the best representation of lung features. The use of random search makes the tuning process faster and improves the efficiency and accuracy of the DNN model. An extensive set of experiments are conducted on a public dataset of CXR images using a set of evaluation metrics. The experiments show that the approach achieved 98.90% accuracy with an increase of 0.47% compared to the latest approach on the same dataset. Moreover, the experimental results demonstrate the approach that the average execution time for detection is very low, confirming its suitability for energy-efficient medical systems.

Highlights

One of the most important diseases prevalent all across the globe is pneumonia, which affects the lungs
The typical convolutional neural networks (CNNs) model architecture usually comprises some pairs of convolution and pooling layers
It is selected due to its ability to provide the best representation of lung features and its capability of achieving a high accuracy detection rate for the diagnosis of pneumonia from Chest X-ray (CXR) images [58]

Summary

Introduction

One of the most important diseases prevalent all across the globe is pneumonia, which affects the lungs. Pneumonia is a deadly disease that causes the air sacs in the lungs to become filled with pus and liquid [1]. Pneumonia is divided into two important types, which are bacterial type and viral type. Bacterial pneumonia causes more severe symptoms [2]. Each type of pneumonia has a different treatment plan from the other. The main cause of pneumonia illness is the high level of pollution around the globe. It is ranked the eighth most common cause of death in the United States [4]. Many steps can be followed to protect children from pneumonia, and these steps could be implemented with low-cost treatment, simple interventions, and low-tech care and medication [6]

Methods

Results

Conclusion