Project Abstract

Pneumonia is a considerable health concern globally, influencing millions of individuals each year. It is an infection that causes fluid or pus to accumulate in the air sacs of the lungs, leading to symptoms such as cough, fever, and breathing difficulties. Chest X-ray (CXR) is widely used in medical imaging and is highly valuable for identifying life-threatening illnesses. This project aims to utilise deep learning methodologies. Specifically, Convolutional Neural Networks (CNNs), facilitate precise medical diagnoses of pneumonia through chest X-ray images and the PyTorch framework. The motivation behind this project stems from the need to improve diagnostic accuracy and efficiency for patients. Assist radiologists in enhancing their decision-making process, minimising the amount of time needed for diagnosis, and leading to improved patient outcomes. Additionally, it decreases the burden on healthcare systems and facilitates prompt medical care. Furthermore, the complexity and the rise of data in healthcare refer to the fact that deep learning technologies will increasingly be utilised within the field.

Keywords: Artificial Intelligence, Convolutional Neural Networks,

 Conference Details

Session: Presentation Stream 3 at Presentation Slot 10

Location: GH029 at Tuesday 7th 13:30 – 17:00

Markers: Deb Roy, Solmaz safari

Course: MSc Data Science, Masters PG

Future Plans: I’m continuing studies