# Acute Lymphoblastic Leukemia Classification from Microscopic Images using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1906.09020 · HTML: https://mlsanity.com/paper/1906.09020/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks](https://mlsanity.com/paper/2105.03995/) (2021)
- [Enhanced Leukemic Cell Classification Using Attention-Based CNN and Data Augmentation](https://mlsanity.com/paper/2601.01026/) (2026)
- [A Leakage-Aware Comparative Benchmark of Machine Learning, Deep Learning, and Transformer Models for Reliable Leukemia Detection](https://mlsanity.com/paper/2606.24944/) (2026)
- [Detecting DeFi Securities Violations from Token Smart Contract Code](https://mlsanity.com/paper/2112.02731/) (2021)

## Abstract (arXiv)

Examining blood microscopic images for leukemia is necessary when expensive equipment for flow cytometry is unavailable. Automated systems can ease the burden on medical experts for performing this examination and may be especially helpful to quickly screen a large number of patients. We present a simple, yet effective classification approach using a ResNeXt convolutional neural network with Squeeze-and-Excitation modules. The approach was evaluated in the C-NMC online challenge and achieves a weighted F1-score of 88.91% on the test set. Code is available at https://github.com/jprellberg/isbi2019cancer