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 - http://www.scopus.com/inward/record.url?eid=2-s2.0-85172407491&partnerID=MN8TOARS
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 -