# A CNN-LSTM Architecture for Detection of Intracranial Hemorrhage on CT scans

arXiv: https://arxiv.org/abs/2005.10992 · HTML: https://mlsanity.com/paper/2005.10992/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2020.

## Built on by (most cited first, 5 of 5)

- [Weakly supervised deep learning-based intracranial hemorrhage localization](https://mlsanity.com/paper/2105.00781/) (2021)
- [Class Activation Map-based Weakly supervised Hemorrhage Segmentation using Resnet-LSTM in Non-Contrast Computed Tomography images](https://mlsanity.com/paper/2309.16627/) (2023)
- [Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection](https://mlsanity.com/paper/2403.14829/) (2024)
- [TomoGraphView: 3D Medical Image Classification with Omnidirectional Slice Representations and Graph Neural Networks](https://mlsanity.com/paper/2511.09605/) (2025)
- [Res-Dense Net for 3D Covid Chest CT-scan classification](https://mlsanity.com/paper/2208.04613/) (2022)

## Abstract (arXiv)

We propose a novel method that combines a convolutional neural network (CNN) with a long short-term memory (LSTM) mechanism for accurate prediction of intracranial hemorrhage on computed tomography (CT) scans. The CNN plays the role of a slice-wise feature extractor while the LSTM is responsible for linking the features across slices. The whole architecture is trained end-to-end with input being an RGB-like image formed by stacking 3 different viewing windows of a single slice. We validate the method on the recent RSNA Intracranial Hemorrhage Detection challenge and on the CQ500 dataset. For the RSNA challenge, our best single model achieves a weighted log loss of 0.0522 on the leaderboard, which is comparable to the top 3% performances, almost all of which make use of ensemble learning. Importantly, our method generalizes very well: the model trained on the RSNA dataset significantly outperforms the 2D model, which does not take into account the relationship between slices, on CQ500. Our codes and models is publicly avaiable at https://github.com/VinBDI-MedicalImagingTeam/midl2020-cnnlstm-ich.