Affiliations: Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia
Correspondence:
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Corresponding author. Vinayakumar Ravi, Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia. E-mail: vravi@pmu.edu.sa.
Note: [1] ORCID: 0000-0001-6873-6469
Abstract: Deep learning-based models are employed in computer-aided diagnosis (CAD) tools development for pediatric pneumonia (P-Pneumonia) detection. The accuracy of the model depends on the scaling of the deep learning model. A survey on deep learning shows that models with a greater number of layers achieve better performances for P-Pneumonia detection. However, the identification of the optimal models is considered to be important work for P-Pneumonia detection. This work presents a hybrid deep learning model for P-Pneumonia detection. The model leverages the EfficientNetV2 model that employs various advanced methodologies to maintain the balance between the model scaling and the performance of the model in P-Pneumonia detection. The features of EfficientNetV2 models are passed into global weighted average pooling (GWAP) which acts like an attention layer. It helps to extract the important features that point to the infected regions of the radiography image and discard all the unimportant information. The features from GWAP are high in dimension and using kernel-based principal component analysis (K-PCA), the features were reduced. Next, the reduced features are combined together and passed into a stacked classifier. The stacked classifier is a two-stage approach in which the first stage employs a support vector machine (SVM) and random forest tree (RFT) for the prediction of P-Pneumonia using the fused features and logistic regression (LRegr) on values of prediction for classification. Detailed experiments were done for the proposed method in P-Pneumonia detection using publically available benchmark datasets. Various settings in the experimental analysis are done to identify the best model. The proposed model outperformed the other methods by improving the accuracy by 4% in P-Pneumonia detection. To show that the proposed model is robust, the model performances were shown on the completely unseen dataset of P-Pneumonia. The hybrid deep learning-based P-Pneumonia model showed good performance on completely unseen data samples of P-Pneumonia patients. The generalization of the proposed P-Pneumonia model is studied by evaluating the model on similar lung diseases such as COVID-19 (CV-19) and Tuberculosis (TBS). In all the experiments, the P-Pneumonia model has shown good performances on similar lung diseases. This indicates that the model is robust and generalizable on data samples of different patients with similar lung diseases. The P-Pneumonia models can be used in healthcare and clinical environments to assist doctors and healthcare professionals in improving the detection rate of P-Pneumonia.
