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

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.

Automatic Detection of Pneumonia using Concatenated Convolutional Neural Network

“…When comparing our results with those of other models, we got good results on most metrics, except for recall (Figs. [19][20][21][22][23]. Finally, we can also say that the proposed approach was able to outperform many previous studies in terms of reaching stable performance and among these studies that reached uctuating performance regardless of the results as the values [22][23][24], [27], and [44].…”

“…In [19], a DenseNet-121 pre-trained model is used as a feature extractor with a deep neural network as a classi er. The best accuracy reached is 98% after conducting three experiments on the model, while the shape of the performance curve was unstable in the rst and third experiments and reached a more stable curve in the second experiment.…”

DeePNeu: Robust Detection of Pneumonia Symptoms using Faster R-CNN

“…The use of random search speeds up the setup process and improves the DNN model's efficiency and accuracy. The model demonstrates that the strategy achieves a 98.90% accuracy based on the experimental results [24].…”

Wireless sensor and wireless body area network assisted biosensor network for effective monitoring and prevention of non-ventilator hospital-acquired pneumonia

“…Surveillance gathers information that is crucial for assessing the scale of the issue, highlighting highrisk patients, and assessing the efficacy of preventive efforts. Antibodies, lifestyle, environmental factors, hospitalization, ICU, and ventilator admission all increase the risk of pneumonia (Almaslukh, 2021). Many instances of pneumonia may be prevented by pneumococcal vaccinations, but they cannot prevent all of them.…”