# RADNET: Radiologist Level Accuracy using Deep Learning for HEMORRHAGE detection in CT Scans

arXiv: https://arxiv.org/abs/1710.04934 · HTML: https://mlsanity.com/paper/1710.04934/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2017.

## Built on by (most cited first, 17 of 17)

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- [Hemorica: A Comprehensive CT Scan Dataset for Automated Brain Hemorrhage Classification, Segmentation, and Detection](https://mlsanity.com/paper/2509.22993/) (2025)
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- [Machine Learning Applications in Traumatic Brain Injury: A Spotlight on Mild TBI](https://mlsanity.com/paper/2401.03621/) (2024)
- [An Automatic Cascaded Model for Hemorrhagic Stroke Segmentation and Hemorrhagic Volume Estimation](https://mlsanity.com/paper/2401.04570/) (2024)
- [A Novel Multi-branch ConvNeXt Architecture for Identifying Subtle Pathological Features in CT Scans](https://mlsanity.com/paper/2510.09107/) (2025)
- [CT Scans As Video: Efficient Intracranial Hemorrhage Detection Using Multi-Object Tracking](https://mlsanity.com/paper/2601.02521/) (2026)

## Abstract (arXiv)

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.