Classification of Normal Versus Malignant Cells in B-ALL Microscopic Images Based on a Tiled Convolution Neural Network Approach

  • Pouyan MohajeraniEmail author
  • Vasilis Ntziachristos
Conference paper
Part of the Lecture Notes in Bioengineering book series (LNBE)


In this paper we present a method based on the existing convolution neural network architecture of AlexNet for the purpose of classifying microscopic images of B-ALL white blood cancer cells. This classification problem is especially challenging due to lack of conspicuous morphological differences between normal and malignant cell nuclei. Therefore, we designed a machine learning pipeline that focused on the texture of the staining images. Briefly, our approach divides the cell image into several overlapping tiles and trains a modified version of AlexNet on the tiles. Only those tiles are retained which are fully contained within the cell image. Several such networks were trained in an ensemble fashion using different trainingvalidation data splits. For a given test image, the tiles are generated and ran through all the trained networks. The outputs of all networks along with the nucleus area are then fed into a simple decision tree, which generates the final prediction. The proposed method was developed in the context of the ISBI 2019 C-NMC challenge. The final testing results demonstrated a classification-weighted F1 score of 0.8307 using 2586 test images. The results demonstrate the possibility of making relatively accurate predictions using only local texture features.


Convolution neural network Machine learning Deep learning Cell classification Acute lymphoblastic leukemia 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Chair of Biological ImagingTechnische Universität MünchenMunichGermany
  2. 2.Institute of Biological and Medical Imaging, Helmholtz Zentrum MünchenNeuherbergGermany

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