Acute Lymphoblastic Leukemia Classification from Microscopic Images using Convolutional Neural Networks

TL;DR

This paper introduces a convolutional neural network-based method for classifying acute lymphoblastic leukemia from microscopic blood images, aiming to assist medical diagnosis especially where advanced equipment is unavailable.

Contribution

The paper proposes a ResNeXt CNN with Squeeze-and-Excitation modules for leukemia classification, demonstrating high accuracy in a competitive challenge setting.

Findings

Achieved a weighted F1-score of 88.91% on the test set.

Effective approach for leukemia detection using microscopic images.

Code available for reproducibility and further research.

Abstract

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