A new approach for the detection of pneumonia in children using CXR images based on an real-time IoT system

Abstract

Pneumonia is responsible for high infant morbidity and mortality. This disease affects the small air sacs (alveoli) in the lung and requires prompt diagnosis and appropriate treatment. Chest X-rays are one of the most common tests used to detect pneumonia. In this work, we propose a real-time Internet of Things (IoT) system to detect pneumonia in chest X-ray images. The dataset used has 6000 chest X-ray images of children, and three medical specialists performed the validations. In this work, twelve different architectures of Convolutional Neural Networks (CNNs) trained on ImageNet were adapted to operate as the resource extractors. Subsequently, the CNNs were combined with consolidated learning methods, such as k-Nearest Neighbor (kNN), Naive Bayes, Random Forest, Multilayer Perceptron (MLP), and Support Vector Machine (SVM). The results showed that the VGG19 architecture with the SVM classifier using the RBF kernel was the best model to detect pneumonia in these chest radiographs. This combination reached 96.47%, 96.46%, and 96.46% for Accuracy, F1 score, and Precision values, respectively. Compared to other works in the literature, the proposed approach had better results for the metrics used. These results show that this approach for the detection of pneumonia in children using a real-time IoT system is efficient and is, therefore, a potential tool to aid in medical diagnoses. This approach will allow specialists to obtain faster and more accurate results and thus provide the appropriate treatment.

Keywords: Convolutional neural networks; Pneumonia detection; Real-time IoT system; Transfer learning.

Fig. 1

a Example of pulmonary opacities; b Normal chest radiography showing the main identifiable anatomical structures (LA left atrium, LV left ventricle, AD right atrium)

Fig. 2

Samples of each class of the CXR dataset. From left to right, sets of five images for the classes a normal, b pneumonia

Fig. 3

Samples of each class, for classes a normal and b pneumonia, from the chest radiography data set. From left to right, we compare the original sample and the preprocessed one, with a highlight after preprocessing

Fig. 4

Method flow

Fig. 5

LINDA system structure

Fig. 6

The confusion matrix structure

Fig. 7

Accuracy, and testing time for the best combinations of feature extractor with classifier. (C-1: MobileNet + SVM(Polynomial), C-2: DenseNet169 + MLP, C-3: DenseNet169 + SVM(RBF), C-4: DenseNet201 + MLP, C-5: DenseNet201 + SVM(RBF), C-6: VGG16 + SVM(RBF), and C-7: VGG19 + SVM(RBF)