CNN-Based Models for Accurate Prediction of Pediatric Pneumonia in Chest X-rays

Children pneumonia is now one of the biggest health concerns throughout the world and accounts for high mortality and morbidity in areas of low healthcare Sufficiency. Tumour Rectangle: Timely identification of cancer and credit diagnosis play a very important role in improving the quality of patient care. Chest X-ray is a traditional way for the diagnosis supported by reduced inter-observer agreement and slow output interpretation, which can hinder the activation of medical treatment. This research work therefore aims at developing an automated pneudetect system by utilizing deep learning algorithm known as convolution neural networks (CNNs) for accurate diagnosis of pediatric pneumonia from chest X-ray images. To improve the model, data augmentation and transfer learning employing architectures such as VGG16 the model was trained with the efficiency in detecting and classifying pneumonia cases in chest X-rays of children. In this structure, the model obtained an accuracy of 90.82% and final loss of 0.3058 distinguishing between healthy and pneumonia affected lungs. It shows that deep learning can enhance competency and accuracy in diagnostic outcomes in addressing gaps which currently exist in practice. But the generalization of this model is recommended to be confirmed with altogether different datasets and clinical data subsequently for enriching the model with real clinical scenarios. This work shows how such function could be implemented to alter the future of medical imaging, and predicting the timeline for finding pneumonia among children more accurately. Integrating deep learning to operate as part of the clinical care delivery systems holds the potential to revolutionize diagnosis in paediatric patients with Respiratory diseases.