Abstract
Breast cancer is a prevalent and potentially deadly disease affecting women in the UK, with 1 in 7 women and 1 in 1000 men at risk. Early diagnosis, effective treatment, awareness, lifestyle choices, genetic testing, and research efforts have helped reduce mortality and improve patient outcomes. Extensive research has enhanced our understanding of the disease and led to better patient survival rates and quality of life. However, breast cancer remains a significant global health challenge, requiring ongoing research and innovation. This paper discusses using machine learning and deep learning techniques, including Convolutional Neural Networks, Transfer Learning, and Ensemble Learning, to analyze a dataset primarily consisting of images. The main goal is to compare these methods based on performance, focusing on applying effective pre-processing techniques. Using the Digital Database for Screening Mammography (DDMS) dataset, CNN exhibited favorable accuracy, and ResNet reached an impressive 93%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cancer Research UK https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/breast-cancer
The Complete Mini-DDSM https://www.kaggle.com/datasets/cheddad/miniddsm2(Accessed: September 2023)
Nemade V, Pathak S, Dubey AK (2022) A systematic literature review of breast cancer diagnosis using machine intelligence techniques 29:4401–4430. https://doi.org/10.1007/s11831-022-09738-3 (Accessed: September 2023)
Abdelhafiz D, Yang C, Ammar R, Nabavi S (2019) Deep convolutional neural networks for mammography: advances, challenges and applications
da Rocha SV, Junior GB, Silva AC, de Paiva AC, Gattass M (2016) Texture analysis of masses malignant in mammograms images using a combined approach of diversity index and local binary patterns distribution. Expert Syst Appl 66:7–19
Dhungel N, Carneiro G, Bradley AP (2017) A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med Image Anal 37:114–128
Suzuki S, Zhang X, Homma N, Ichiji K, Sugita N, Kawasumi Y, Ishibashi T, Yoshizawa M (2016) Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis. In: 2016 55th annual conference of the Society of Instrument and control engineers of Japan (SICE). IEEE, pp 1382–1386
Samala RK, Chan HP, Hadjiiski L, Helvie MA, Richter CD, Cha KH (2018) Breast cancer diagnosis in digital breast tomosynthesis: effects of training sample size on multi-stage trans fer learning using deep neural nets. IEEE Trans Med Imaging 38(3):686–696
Mohanty F, Rup S, Dash B, Majhi B, Swamy MN (2020) An improved scheme for digital mammogram classification using weighted chaotic salp swarm algorithm-based kernel extreme learning machine. Appl Soft Comput 91:106266
Vijayarajeswari R, Parthasarathy P, Vivekanandan S, Basha AA (2019) Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform. Measurement 146:800–805
Mohanty F, Rup S, Dash B, Majhi B, Swamy MN (2020) improved scheme for digital mammogram classification using weighted chaotic salp swarm algorithm-based kernel extreme learning machine. Appl Soft Comput 91:106266
Chougrad H, Zouaki H, Alheyane O (2020) Multi-label transfer learning for the early diagnosis of breast cancer. Neurocomputing 392:168–180
Rahman AS, Belhaouari SB, Bouzerdoum A, Baali H, Alam T, El-daraa AM (2020) Breast mass tumor classifcation using deep learning. In: 2020 IEEE international conference on informatics, IoT, and enabling technologies (ICIoT). IEEE, pp 271-276
Alkhaleefah M, Chittem PK, Achhannagari VP, Ma SC, Chang YL (2020) The infuence of image augmentation on breast lesion classification using transfer learning. In: 2020 International conference on artifcial intelligence and signal processing (AISP). IEEE, pp 1-5
de Oliveira FS, de Carvalho Filho AO, Silva AC, de Paiva AC, Gattass M (2015) Classifcation of breast regions as mass and non-mass based on digital mammograms using taxonomic indexes and SVM. Comput Biol Med 57:42-53 https://github.com/rasbt/stat453-deep-learning-ss21/tree/main/L14/2-resnet-ex
PyTorch (2017) ImageFolder - Torchvision 0.14 documentation, pytorch.org. Available at: https://pytorch.org/vision/stable/generated/torchvision.datasets.ImageFolder.html (Accessed: September 2023)
Swiderski B, Osowski S, Kurek J, Kruk M, Lugowska I, Rutkowski P, Barhoumi W (2017) Novel methods of image description and ensemble of classifiers in application to mammogram analysis. Expert Syst Appl 81:67–78
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yanga, O.K.N., Homayounvala, E. (2024). Breast Cancer Prediction and Detection: Comparison of the Latest Machine Learning Techniques. In: Hassanien, A.E., Anand, S., Jaiswal, A., Kumar, P. (eds) Innovative Computing and Communications. ICICC 2024. Lecture Notes in Networks and Systems, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-97-3817-5_3
Download citation
DOI: https://doi.org/10.1007/978-981-97-3817-5_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-3816-8
Online ISBN: 978-981-97-3817-5
eBook Packages: EngineeringEngineering (R0)