International Research Journal of Education and Technology

Pneumonia detection and segmentation in chest X rays are pivotal tasks in medical imaging, facilitating timely diagnosis and treatment of this life threatening condition. In this study, we present a novel approach for automating pneumonia lung opacity detection and segmentation using transfer learning of the Mask R-CNN architecture. By leveraging a pre-trained model on a large dataset of annotated chest X rays, we fine tune the network to effectively identify and delineate regions indicative of pneumonia opacities. Our methodology integrates image preprocessing, feature extraction, region proposal, and pixel-level segmentation to achieve accurate localization and segmentation of pneumonia related abnormalities. We evaluate the performance of our approach on a diverse dataset and compare it against existing techniques, demonstrating superior accuracy and robustness. The proposed method holds significant promise in enhancing pneumonia diagnosis, potentially improving patient outcomes and streamlining healthcare workflows.

Keyword: Pneumonia, lung opacity detection, pixel-level segmentation, diagnosis.