Paper Contents
Abstract
This paper presents a deep learning-based system for automated pneumonia detection, leveraging convolutional neural networks (CNN) to analyze medical images, specifically X-rays and MRIs. The proposed system is developed using TensorFlow, focusing on enhancing the sensitivity and accuracy of diagnostic performance. By integrating multiple imaging modalities, the model offers better generalization across diverse datasets, making it suitable for clinical use. Experimental results demonstrate that the model outperforms traditional diagnostic methods, showing improved sensitivity and precision. The findings highlight the potential of deep learning tools to aid clinicians in identifying pneumonia cases efficiently, thus facilitating faster and more accurate treatment. Future work will focus on incorporating additional imaging types and interpretability frameworks to enhance transparency in decision-making.
Copyright
Copyright © 2024 Gunjan Thakre. This is an open access article distributed under the Creative Commons Attribution License.
