Abstract

Pneumonia accounts for over 15% of all deaths of children under 5 years old internationally. In 2015, 920,000 children under the age of 5 died from the disease. In this challenge, we were challenged to build an algorithm to detect a visual signal for pneumonia in medical images. Layer 6 collaborated with 16Bit and developed an ensemble of 15 state-of-the-art object detection models (10 Mask RCNN, 3 YOLOv3, and 2 Faster RCNN models), in combination with a classifier (DenseNet-121architecture pre-trained on NIH Chest X-rays data set) that served to reduce false positives, to detect pneumonia chest X-rays. We found that using a relaxed detection threshold for object detection, whilst requiring unanimous agreement among the detectors, effectively consolidated the need to minimize both false positives and false negatives. Adaptive histogram equalization was used to improve image contrast as a data preprocessing step. We used age, sex, and view position as inputs into the penultimate layer of the classifier to improve performance.