  We describe a deep learning approach for automated brain hemorrhage detection
from computed tomography (CT) scans. Our model emulates the procedure followed
by radiologists to analyse a 3D CT scan in real-world. Similar to radiologists,
the model sifts through 2D cross-sectional slices while paying close attention
to potential hemorrhagic regions. Further, the model utilizes 3D context from
neighboring slices to improve predictions at each slice and subsequently,
aggregates the slice-level predictions to provide diagnosis at CT level. We
refer to our proposed approach as Recurrent Attention DenseNet (RADnet) as it
employs original DenseNet architecture along with adding the components of
attention for slice level predictions and recurrent neural network layer for
incorporating 3D context. The real-world performance of RADnet has been
benchmarked against independent analysis performed by three senior radiologists
for 77 brain CTs. RADnet demonstrates 81.82% hemorrhage prediction accuracy at
CT level that is comparable to radiologists. Further, RADnet achieves higher
recall than two of the three radiologists, which is remarkable.
