Abstract:
This study tries to create a reliable model for detecting pneumonia using chest X-ray
images. The work makes use of advanced deep learning architectures such as DenseNet,
InceptionV3, ResNet, VGG16, and VGG19, as well as Google Colab's tremendous
computing capabilities and a Kaggle data set. To achieve high accuracy and reliability, the
project goes through rigorous preprocessing, training, and validation processes. Key
performance parameters, such as precision, recall, and F1-score, are used to assess each
model's success. The findings show significant gains in detection accuracy, showing the
utility of deep learning models in medical picture processing. This research gives useful
insights for healthcare practitioners and contributes to the development of automated
diagnostic systems aimed at improving pneumonia identification and treatment.
