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article

Mammogram classification using dynamic time warping

Authors:

Syed Jamal Gardezi,

Jose M. Sanchez Bornot,

Mohammad HussainAuthors Info & Claims

Multimedia Tools and Applications, Volume 77, Issue 3

Pages 3941 - 3962

https://doi.org/10.1007/s11042-016-4328-8

Published: 01 February 2018 Publication History

Abstract

This paper presents a new approach for breast cancer classification using time series analysis. In particular, the region of interest (ROI) in mammogram images is classified as normal or abnormal using dynamic time warping (DTW) as a similarity measure. According to the analogous case in time series analysis, the DTW subsumes Euclidean distance (ED) as a specific case with increased robustness due to DTW flexibility to address local horizontal/vertical deformations. This method is especially attractive for biomedical image analysis and is applied to mammogram classification for the first time in this paper. The current study concludes that varying the size of the ROI images and the restriction on the search criteria for the warping path do not affect the performance because the method produces good classification results with reduced computational complexity. The method is tested on the IRMA and MIAS dataset using the k-nearest neighbour classifier for different k values, which produces an area under curve (AUC) value of 0.9713 for one of the best scenarios.

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Cited By

Kaur JKaur P(2024)A systematic literature analysis of multi-organ cancer diagnosis using deep learning techniquesComputers in Biology and Medicine10.1016/j.compbiomed.2024.108910179:COnline publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108910
Shen L(2022)Classification and Retrieval of Multimedia Book Cloud Resources Based upon Hash AlgorithmMobile Information Systems10.1155/2022/93239492022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/9323949
Bhatt CKumar IVijayakumar VSingh KKumar A(2021)The state of the art of deep learning models in medical science and their challengesMultimedia Systems10.1007/s00530-020-00694-127:4(599-613)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s00530-020-00694-1

Mammogram classification using dynamic time warping
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms

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Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 77, Issue 3

February 2018

1114 pages

ISSN:1380-7501

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Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 February 2018

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Cited By

Kaur JKaur P(2024)A systematic literature analysis of multi-organ cancer diagnosis using deep learning techniquesComputers in Biology and Medicine10.1016/j.compbiomed.2024.108910179:COnline publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108910
Shen L(2022)Classification and Retrieval of Multimedia Book Cloud Resources Based upon Hash AlgorithmMobile Information Systems10.1155/2022/93239492022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/9323949
Bhatt CKumar IVijayakumar VSingh KKumar A(2021)The state of the art of deep learning models in medical science and their challengesMultimedia Systems10.1007/s00530-020-00694-127:4(599-613)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s00530-020-00694-1

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