Classification of cancer microscopic images via convolutional neural networks

Mohammad Azam Khan; Jaegul Choo

doi:10.1007/978-981-15-0798-4_15

Classification of cancer microscopic images via convolutional neural networks

Mohammad Azam Khan, Jaegul Choo

Research output: Chapter in Book/Report/Conference proceeding › Chapter

11 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Lecture Notes in Bioengineering
Publisher	Springer
Pages	141-147
Number of pages	7
DOIs	https://doi.org/10.1007/978-981-15-0798-4_15
Publication status	Published - 2019

Publication series

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

Bibliographical note

Publisher Copyright:
© Springer Nature Singapore Pte Ltd 2019.

Keywords

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

ASJC Scopus subject areas

Biotechnology
Bioengineering
Applied Microbiology and Biotechnology
Biomedical Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-981-15-0798-4_15

Cite this

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abstract = "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.",

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AB - 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.

KW - B-lymphoblast cell

KW - B-lymphoid

KW - Blood cancer

KW - Blood smear

KW - Convolutional neural networks

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PB - Springer

ER -

Classification of cancer microscopic images via convolutional neural networks

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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