Deep learning-based mobile application for the enhancement of pneumonia medical imaging analysis: A case-study of West-Meru Hospital
Japheth Mumo Kimeu, Michael Kisangiri, Hope Mbelwa, Judith Leo
Abstract
Pneumonia remains a significant global health challenge, demanding innovative solutions. This study presents a novel approach to pneumonia diagnosis and medical imaging analysis, leveraging advanced technologies. The study used a Literature Review Methodology to study various scientific articles and involved healthcare staff, including Doctors, Nurses, Radiologists and the community, in sharing their requirements for the study. The findings led to the proposal for the integration of Deep Learning techniques, including Convolutional Neural Network (CNN), as well as tools like YOLOv8, Roboflow, and Ultralytics, to revolutionize pneumonia detection and classification. The EfficientDet-Lite2 model architecture was subsequently used to generate a TensorFlow Lite Model, deployable in both Android and iOS mobile applications. The study’s outcomes reveal a substantial improvement in the precision and recall metrics. These results signify a promising step forward in empowering healthcare professionals with timely and reliable results for optimal patient management. • Proposed a novel method for pneumonia diagnosis based on object detection. • Applied YoloV8 and EfficientDet2, to detect and classify three pneumonia classes. • Developed LungGuard, a mobile app for a convenient pneumonia diagnosis. • Achieved higher accuracy, precision, and recall than the existing methods. • Highlighted object detection’s effectiveness for pneumonia diagnosis.