Colour Clustering and Deep Transfer Learning Techniques for Breast Cancer Detection Using Mammography Images

Hosameldin O. A. Ahmed; Asoke K. Nandi

doi:10.1007/978-3-031-38430-1_9

Colour Clustering and Deep Transfer Learning Techniques for Breast Cancer Detection Using Mammography Images

Hosameldin O. A. Ahmed
, Asoke K. Nandi

Research output: Chapter in Book/Published conference output › Conference publication

4 Citations (Scopus)

Abstract

Breast cancer is a major global health concern affecting millions of women each year. Computer-aided diagnosis (CAD) systems have the potential to contribute significantly to early detection and reducing the mortality rate of breast cancer. This paper proposes a new methodology for breast cancer detection utilising data analytics, artificial intelligence, and mammograms. The approach is a mixed methodology based on colour clustering and deep transfer learning techniques to extract features from mammogram images. The proposed method was validated using the mini-DDSM mammogram images dataset, and its effectiveness was evaluated using various metrics such as accuracy, specificity, precision, recall, and F1 score. The results showed that all networks had high detection accuracy, with GoogleNet achieving the highest (99.58%) and ShuffleNet the lowest (97.08%). The proposed method achieved 100% detection accuracy using ResNet18, VGG16, ShuffleNet, DarkNet, and NasnetLarge, while Inception-ResNet-v2 had a detection accuracy of 98.33% with LRC and 99.17% with SVM. The proposed method has demonstrated the potential to improve the performance of CAD systems.

Original language	English
Title of host publication	The Latest Developments and Challenges in Biomedical Engineering - Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical Engineering
Editors	Paweł Strumiłło, Artur Klepaczko, Michał Strzelecki, Dorota Bociąga
Publisher	Springer
Pages	105-119
Number of pages	15
ISBN (Print)	9783031384295
DOIs	https://doi.org/10.1007/978-3-031-38430-1_9
Publication status	Published - 12 Sept 2023
Event	The 23rd Polish Conference on Biocybernetics and Biomedical Engineering - Lodz, Poland Duration: 27 Sept 2023 → 29 Sept 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	746 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	The 23rd Polish Conference on Biocybernetics and Biomedical Engineering
Country/Territory	Poland
City	Lodz
Period	27/09/23 → 29/09/23

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Breast cancer detection
Deep transfer learning
Image clustering
Mammography images

Access to Document

10.1007/978-3-031-38430-1_9

Cite this

Ahmed, H. O. A., & Nandi, A. K. (2023). Colour Clustering and Deep Transfer Learning Techniques for Breast Cancer Detection Using Mammography Images. In P. Strumiłło, A. Klepaczko, M. Strzelecki, & D. Bociąga (Eds.), The Latest Developments and Challenges in Biomedical Engineering - Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical Engineering (pp. 105-119). (Lecture Notes in Networks and Systems; Vol. 746 LNNS). Springer. https://doi.org/10.1007/978-3-031-38430-1_9

Ahmed, Hosameldin O. A. ; Nandi, Asoke K. / Colour Clustering and Deep Transfer Learning Techniques for Breast Cancer Detection Using Mammography Images. The Latest Developments and Challenges in Biomedical Engineering - Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical Engineering. editor / Paweł Strumiłło ; Artur Klepaczko ; Michał Strzelecki ; Dorota Bociąga. Springer, 2023. pp. 105-119 (Lecture Notes in Networks and Systems).

@inproceedings{f022146f8b644b88b705495a026b70cf,

title = "Colour Clustering and Deep Transfer Learning Techniques for Breast Cancer Detection Using Mammography Images",

abstract = "Breast cancer is a major global health concern affecting millions of women each year. Computer-aided diagnosis (CAD) systems have the potential to contribute significantly to early detection and reducing the mortality rate of breast cancer. This paper proposes a new methodology for breast cancer detection utilising data analytics, artificial intelligence, and mammograms. The approach is a mixed methodology based on colour clustering and deep transfer learning techniques to extract features from mammogram images. The proposed method was validated using the mini-DDSM mammogram images dataset, and its effectiveness was evaluated using various metrics such as accuracy, specificity, precision, recall, and F1 score. The results showed that all networks had high detection accuracy, with GoogleNet achieving the highest (99.58\%) and ShuffleNet the lowest (97.08\%). The proposed method achieved 100\% detection accuracy using ResNet18, VGG16, ShuffleNet, DarkNet, and NasnetLarge, while Inception-ResNet-v2 had a detection accuracy of 98.33\% with LRC and 99.17\% with SVM. The proposed method has demonstrated the potential to improve the performance of CAD systems.",

keywords = "Breast cancer detection, Deep transfer learning, Image clustering, Mammography images",

author = "Ahmed, \{Hosameldin O. A.\} and Nandi, \{Asoke K.\}",

year = "2023",

month = sep,

day = "12",

doi = "10.1007/978-3-031-38430-1\_9",

language = "English",

isbn = "9783031384295",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer",

pages = "105--119",

editor = "Pawe{\l} Strumi{\l}{\l}o and Artur Klepaczko and Micha{\l} Strzelecki and Dorota Boci{\c a}ga",

booktitle = "The Latest Developments and Challenges in Biomedical Engineering - Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical Engineering",

address = "Germany",

note = "The 23rd Polish Conference on Biocybernetics and Biomedical Engineering ; Conference date: 27-09-2023 Through 29-09-2023",

}

Ahmed, HOA & Nandi, AK 2023, Colour Clustering and Deep Transfer Learning Techniques for Breast Cancer Detection Using Mammography Images. in P Strumiłło, A Klepaczko, M Strzelecki & D Bociąga (eds), The Latest Developments and Challenges in Biomedical Engineering - Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical Engineering. Lecture Notes in Networks and Systems, vol. 746 LNNS, Springer, pp. 105-119, The 23rd Polish Conference on Biocybernetics and Biomedical Engineering, Lodz, Poland, 27/09/23. https://doi.org/10.1007/978-3-031-38430-1_9

Colour Clustering and Deep Transfer Learning Techniques for Breast Cancer Detection Using Mammography Images. / Ahmed, Hosameldin O. A.; Nandi, Asoke K.
The Latest Developments and Challenges in Biomedical Engineering - Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical Engineering. ed. / Paweł Strumiłło; Artur Klepaczko; Michał Strzelecki; Dorota Bociąga. Springer, 2023. p. 105-119 (Lecture Notes in Networks and Systems; Vol. 746 LNNS).

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - Colour Clustering and Deep Transfer Learning Techniques for Breast Cancer Detection Using Mammography Images

AU - Ahmed, Hosameldin O. A.

AU - Nandi, Asoke K.

PY - 2023/9/12

Y1 - 2023/9/12

N2 - Breast cancer is a major global health concern affecting millions of women each year. Computer-aided diagnosis (CAD) systems have the potential to contribute significantly to early detection and reducing the mortality rate of breast cancer. This paper proposes a new methodology for breast cancer detection utilising data analytics, artificial intelligence, and mammograms. The approach is a mixed methodology based on colour clustering and deep transfer learning techniques to extract features from mammogram images. The proposed method was validated using the mini-DDSM mammogram images dataset, and its effectiveness was evaluated using various metrics such as accuracy, specificity, precision, recall, and F1 score. The results showed that all networks had high detection accuracy, with GoogleNet achieving the highest (99.58%) and ShuffleNet the lowest (97.08%). The proposed method achieved 100% detection accuracy using ResNet18, VGG16, ShuffleNet, DarkNet, and NasnetLarge, while Inception-ResNet-v2 had a detection accuracy of 98.33% with LRC and 99.17% with SVM. The proposed method has demonstrated the potential to improve the performance of CAD systems.

AB - Breast cancer is a major global health concern affecting millions of women each year. Computer-aided diagnosis (CAD) systems have the potential to contribute significantly to early detection and reducing the mortality rate of breast cancer. This paper proposes a new methodology for breast cancer detection utilising data analytics, artificial intelligence, and mammograms. The approach is a mixed methodology based on colour clustering and deep transfer learning techniques to extract features from mammogram images. The proposed method was validated using the mini-DDSM mammogram images dataset, and its effectiveness was evaluated using various metrics such as accuracy, specificity, precision, recall, and F1 score. The results showed that all networks had high detection accuracy, with GoogleNet achieving the highest (99.58%) and ShuffleNet the lowest (97.08%). The proposed method achieved 100% detection accuracy using ResNet18, VGG16, ShuffleNet, DarkNet, and NasnetLarge, while Inception-ResNet-v2 had a detection accuracy of 98.33% with LRC and 99.17% with SVM. The proposed method has demonstrated the potential to improve the performance of CAD systems.

KW - Breast cancer detection

KW - Deep transfer learning

KW - Image clustering

KW - Mammography images

UR - https://link.springer.com/chapter/10.1007/978-3-031-38430-1_9

UR - https://www.scopus.com/pages/publications/85172407491

U2 - 10.1007/978-3-031-38430-1_9

DO - 10.1007/978-3-031-38430-1_9

M3 - Conference publication

SN - 9783031384295

T3 - Lecture Notes in Networks and Systems

SP - 105

EP - 119

BT - The Latest Developments and Challenges in Biomedical Engineering - Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical Engineering

A2 - Strumiłło, Paweł

A2 - Klepaczko, Artur

A2 - Strzelecki, Michał

A2 - Bociąga, Dorota

PB - Springer

T2 - The 23rd Polish Conference on Biocybernetics and Biomedical Engineering

Y2 - 27 September 2023 through 29 September 2023

ER -

Ahmed HOA, Nandi AK. Colour Clustering and Deep Transfer Learning Techniques for Breast Cancer Detection Using Mammography Images. In Strumiłło P, Klepaczko A, Strzelecki M, Bociąga D, editors, The Latest Developments and Challenges in Biomedical Engineering - Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical Engineering. Springer. 2023. p. 105-119. (Lecture Notes in Networks and Systems). Epub 2023 Sept 10. doi: 10.1007/978-3-031-38430-1_9

Colour Clustering and Deep Transfer Learning Techniques for Breast Cancer Detection Using Mammography Images

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this