# A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification

arXiv: https://arxiv.org/abs/1909.11866 · HTML: https://mlsanity.com/paper/1909.11866/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

## Built on by (most cited first, 2 of 2)

- [Classification of White Blood Cells Using Machine and Deep Learning Models: A Systematic Review](https://mlsanity.com/paper/2308.06296/) (2023)
- [A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells](https://mlsanity.com/paper/2303.03916/) (2023)

## Abstract (arXiv)

Automatic detection of leukemic B-lymphoblast cancer in microscopic images is very challenging due to the complicated nature of histopathological structures. To tackle this issue, an automatic and robust diagnostic system is required for early detection and treatment. In this paper, an automated deep learning-based method is proposed to distinguish between immature leukemic blasts and normal cells. The proposed deep learning based hybrid method, which is enriched by different data augmentation techniques, is able to extract high-level features from input images. Results demonstrate that the proposed model yields better prediction than individual models for Leukemic B-lymphoblast classification with 96.17% overall accuracy, 95.17% sensitivity and 98.58% specificity. Fusing the features extracted from intermediate layers, our approach has the potential to improve the overall classification performance.