PneumoDeL pneumonia detection and localization in chest radiographs using RetinaNet - Gabriel, Kristianne Arielle; Yap, Richelle; Naval, Prospero C. Jr. (Quezon City College of Engineering, University of the Philippines Diliman, 2019)
Pneumonia diagnosis is proven to be a tedious and time-consuming task for radiologists. A common visual signal of pneumonia is in a form of lung opacity, which is very difficult to detect using the naked eye. Datasets of lung chest radiographs are also often imbalanced towards having a majority of negatively labelled (normal) cases outnumbering the target (with lung opacity) cases. Thus, we propose a new method of detecting and localizing pneumonia on chest radiographs using RetinaNet, a model that is robust on class-imbalanced datasets. Experiments on training single to multiple models of RetinaNet (with different backbones and image resolutions of chest radiographs from the RSNA Pneumonia Detection Challenge) were conducted. The bounding box output of the models were then combined using various image ensembling techniques then post-processed. This approach led to getting a mean average precision (mAP) evaluation of 0.26107, higher than the top-scoring team on the RSNA Pneumonia Detection Challenge.
Thesis (Bachelor of Science in Computer Science)--University of the Philippines Diliman
Keywords: Pneumonia -- Diagnosis; Chest -- Radiography -- Data processing