Classification of cancer microscopic images via convolutional neural networks

Mohammad Azam Khan, Jaegul Choo

Research output: Chapter in Book/Report/Conference proceedingChapter

9 Citations (Scopus)


This paper describes our approach for the classification of normal versus malignant cells in B-ALL white blood cancer microscopic images: ISBI 2019—classification of leukemic B-lymphoblast cells from normal B-lymphoid precursors from blood smear microscopic images. We leverage a state of the art convolutional neural network pretrained with the ImageNet dataset and applied several data augmentation and hyperparameters optimization strategies. Our method obtains an F1 score of 0.83 for the final test set in the competition.

Original languageEnglish
Title of host publicationLecture Notes in Bioengineering
Number of pages7
Publication statusPublished - 2019

Publication series

NameLecture Notes in Bioengineering
ISSN (Print)2195-271X
ISSN (Electronic)2195-2728

Bibliographical note

Publisher Copyright:
© Springer Nature Singapore Pte Ltd 2019.


  • B-lymphoblast cell
  • B-lymphoid
  • Blood cancer
  • Blood smear
  • Convolutional neural networks

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology
  • Biomedical Engineering


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