Open Access Peer-reviewed Research Article

Deep learning based capsule networks for breast cancer classification using ultrasound images

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Submitted   Mar 28, 2024
Published   Aug 27, 2024
Issue    Vol 6 (2024)
DOI   10.25082/CCR.2024.01.002

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Stephen Afrifa corresponding author
[1] Department of Information and Communication Engineering, Tianjin University, Tianjin 300072, China [2] Department of Information Technology and Decision Sciences, University of Energy and Natural Resources, Sunyani 00233, Ghana
Vijayakumar Varadarajan
[1] Research Division, Swiss School of Business and Management, Geneva 1213, Switzerland [2] International Divisions, Ajeenkya D. Y. Patil University, Pune 412105, India
Tao Zhang
Department of Information and Communication Engineering, Tianjin University, Tianjin 300072, China
Peter Appiahene
Department of Information Technology and Decision Sciences, University of Energy and Natural Resources, Sunyani 00233, Ghana
Daniel Gyamfi
Department of Mathematics and Statistics, Saint Louis University, MO 63103, Missouri, USA
Rose-Mary Owusuaa Mensah Gyening
Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi 00233, Ghana
Jacob Mensah
Department of Information Technology and Decision Sciences, University of Energy and Natural Resources, Sunyani 00233, Ghana
Samuel Opoku Berchie
Department of Information Technology and Decision Sciences, University of Energy and Natural Resources, Sunyani 00233, Ghana

Abstract

Purposes: Breast cancer (BC) is a disease in which the breast cells multiply uncontrolled. Breast cancer is one of the most often diagnosed malignancies in women worldwide. Early identification of breast cancer is critical for limiting the impact on affected people's health conditions. The influence of technology and artificial intelligence approaches (AI) in the health industry is tremendous as technology advances. Deep learning (DL) techniques are used in this study to classify breast lumps.
Materials and Methods: The study makes use of two distinct breast ultrasound images (BUSI) with binary and multiclass classification. To assist the models in understanding the data, the datasets are exposed to numerous preprocessing and hyperparameter approaches. With data imbalance being a key difficulty in health analysis, due to the likelihood of not having a condition exceeding that of having the disease, this study applies a cutoff stage to impact the decision threshold in the datasets data augmentation procedures. The capsule neural network (CapsNet), Gabor capsule network (GCN), and convolutional neural network (CNN) are the DL models used to train the various datasets.
Results: The findings showed that the CapsNet earned the maximum accuracy value of 93.62% while training the multiclass data, while the GCN achieved the highest model accuracy of 97.08% when training the binary data. The models were also evaluated using a variety of performance assessment parameters, which yielded consistent results across all datasets.
Conclusion: The study provides a non-invasive approach to detect breast cancer; and enables stakeholders, medical practitioners, and health research enthusiasts a fresh view into the analysis of breast cancer detection with DL techniques to make educated judgements.

Keywords
breast cancer, capsule network, deep learning, Gabor capsule, medical imaging

Article Details

Supporting Agencies
The authors are grateful to Adwoa Afriyie, popularly called Amanpene, and Malcolm Afrifa for their encouragement and advise throughout the studies.
How to Cite
Afrifa, S., Varadarajan, V., Zhang, T., Appiahene, P., Gyamfi, D., Gyening, R.-M. O. M., Mensah, J., & Opoku Berchie, S. (2024). Deep learning based capsule networks for breast cancer classification using ultrasound images. Current Cancer Reports, 6(1), 205-224. https://doi.org/10.25082/CCR.2024.01.002
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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