# Deep Learning based Automatic Detection of Dicentric Chromosome

arXiv: https://arxiv.org/abs/2204.08029 · HTML: https://mlsanity.com/paper/2204.08029/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 17 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://mlsanity.com/paper/2011.08036/) (2020)

## Abstract (arXiv)

Automatic detection of dicentric chromosomes is an essential step to estimate radiation exposure and development of end to end emergency bio dosimetry systems. During accidents, a large amount of data is required to be processed for extensive testing to formulate a medical treatment plan for the masses, which requires this process to be automated. Current approaches require human adjustments according to the data and therefore need a human expert to calibrate the system. This paper proposes a completely data driven framework which requires minimum intervention of field experts and can be deployed in emergency cases with relative ease. Our approach involves YOLOv4 to detect the chromosomes and remove the debris in each image, followed by a classifier that differentiates between an analysable chromosome and a non-analysable one. Images are extracted from YOLOv4 based on the protocols described by WHO-BIODOSNET. The analysable chromosome is classified as Monocentric or Dicentric and an image is accepted for consideration of dose estimation based on the analysable chromosome count. We report an accuracy in dicentric identification of 94.33% on a 1:1 split of Dicentric and Monocentric Chromosomes.