Capsule Neural Networks with Bayesian Optimization for Pediatric Pneumonia Detection from Chest X-Ray Images

Background: Pneumonia in children poses a serious threat to life and health, making early detection critically important. In this regard, artificial intelligence methods can provide valuable support. Methods: Capsule networks and Bayesian optimization are modern techniques that were employed to build effective models for predicting pneumonia from chest X-ray images. The medical images underwent essential preprocessing, were divided into training, validation, and testing sets, and were subsequently used to develop the models. results: an accuracy of 95.1%, sensitivity of 98.9%, specificity of 85.4%, precision (PPV) of 94.8%, negative predictive value (NPV) of 96.2%, F1-score of 96.8%, and a Matthews correlation coefficient (MCC) of 0.877. In addition, the model was complemented with an explainability analysis using Grad-CAM, which demonstrated that its predictions rely predominantly on clinically relevant pulmonary regions. Conclusions: The proposed model demonstrates high accuracy and shows promise for potential use in clinical practice. It may also be applied to other tasks in medical image analysis.