Detecting Pneumonia with a Deep Learning Model and Random Data Augmentation Techniques

Abstract

This research paper presents an investigation into the detection of pneumonia using deep learning models and data augmentation techniques. The study compares and evaluates the performance of different models based on experimental results. The proposed model consists of multiple convolutional layers and maxpooling layers. Extensive experiments were conducted on a dataset, and the results demonstrate the efficiency and accuracy of our approach. The findings highlight the potential of deep learning in pneumonia detection and contribute to the existing body of knowledge in this field. The implications of this research can have a significant impact on improving diagnostic accuracy and patient outcomes. Future research directions could explore further enhancements in the model architecture, investigate additional data augmentation techniques, and consider larger datasets for more comprehensive evaluations.