Innovative AI Solutions for Pneumonia Detection: Exploring DenseNet-161 in Medical Imaging

Particularly in sensitive groups including children, the elderly, and those with compromised immune systems,pneumonia is a major respiratory illness with potentially lethal consequences. Early and accurate diagnosis of pneumonia determines both timeliness of action and effective therapy. In this work, we classify paediatric chest X-rays using DenseNet-161, a strong deep learning network, into two groups: Pneumonia and Normal. Derived from the Beijing Women and Children's Medical Centre, this collection comprises 5,863 X-ray photographs of children throughout the ages of one and five. Strong quality control guarantees that Construct validation, training, and test sets from the dataset— is fit for evaluating artificial intelligence models. DenseNet-161 The material was chosen for its denseness connection topology, which facilitates feature reuse and efficient gradient flow, therefore enabling the model to reach high accuracy with minimum parameters.The model showed to be really good in spotting pneumonia cases with an accuracy of 89.26% and a recall of 99.5%. Though, future studies will address the small number of false positives shown by the 85.6% precision of the model. With an F1-score of 0.921 the model runs in balance between recollection and accuracy. Future research will focus on enhancing accuracy of the model by means of hyperparameter tuning, data augmentation, and integration of additional clinical data and so raise the precision.