# Bone Marrow Cytomorphology Cell Detection using InceptionResNetV2

arXiv: https://arxiv.org/abs/2305.05430 · HTML: https://mlsanity.com/paper/2305.05430/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 24 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 1 of 1)

- [A scoping review of transfer learning research on medical image analysis using ImageNet](https://mlsanity.com/paper/2004.13175/) (2020)

## Abstract (arXiv)

Critical clinical decision points in haematology are influenced by the requirement of bone marrow cytology for a haematological diagnosis. Bone marrow cytology, however, is restricted to reference facilities with expertise, and linked to inter-observer variability which requires a long time to process that could result in a delayed or inaccurate diagnosis, leaving an unmet need for cutting-edge supporting technologies. This paper presents a novel transfer learning model for Bone Marrow Cell Detection to provide a solution to all the difficulties faced for the task along with considerable accuracy. The proposed model achieved 96.19% accuracy which can be used in the future for analysis of other medical images in this domain.