Advanced Image Processing for Breast Cancer Detection Using CNN-Based Transfer Learning on Mammograms

Azhar Amer  Alsoufi

Azhar Amer Alsoufi Department of Electronic Technologies, Northern Technical University, Mosul, Iraq

Keywords: Breast Cancer, Convolutional Neural Network, Deep Learning, Artificial Neural Network, Auto-Encoder, VGG16

Abstract

Breast cancer remains the most commonly diagnosed disease and the second leading cause of death among females. Statistically speaking, roughly one out of every eight American women was diagnosed with breast cancer last year. The precise identification of breast cancer also largely relies upon careful analysis of medical images. Though several Deep Learning (DL) algorithms have been employed to analyses such images, therefore, this study focuses on using a Convolutional Neural Network (CNN) to differentiate between different types of mammograms. The use of CNN in image recognition and visual processing has quickly drawn the attention of scholars. Therefore, in this current research, an approach is presented to extract patches from mammograms and utilize them to train the CNN, whereby the order of the section’s feeds into the classification process. In addition, a transfer learning approach is utilized, in which a model created in the initial phase is later utilized as an initial model. Besides using single and multi-CNN and Artificial Neural Network (ANN) layers, two more approaches—Auto-Encoder and VGG16—are used to evaluate and compare the effectiveness of the models on different datasets.

References

N. Marwah, V. K. Singh, G. S. Kashyap, and S. Wazir, “An analysis of the robustness of UAV agriculture field coverage using multi-agent reinforcement learning,” International Journal of Information Technology, vol. 15, no. 4, pp. 2317–2327, May 2023, doi: 10.1007/s41870-023-01264-0.

G. S. Kashyap, K. Malik, S. Wazir, and R. Khan, “Using Machine Learning to Quantify the Multimedia Risk Due to Fuzzing,” Multimedia Tools and Applications, vol. 81, no. 25, pp. 36685–36698, Oct. 2022, doi: 10.1007/s11042-021-11558-9.

R. Mishra, O. Daescu, P. Leavey, D. Rakheja, and A. Sengupta, “Convolutional neural network for histopathological analysis of osteosarcoma,” Journal of Computational Biology, Mary Ann Liebert, Inc., Mar. 2018, pp. 313–325, doi: 10.1089/cmb.2017.0153.

G. Atteia et al., “Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data,” Computers, Materials & Continua, vol. 75, no. 1, pp. 1883–1900, Feb. 2023, doi: 10.32604/cmc.2023.031723.

H. Muhammad et al., “Unsupervised subtyping of cholangiocarcinoma using a deep clustering convolutional autoencoder,” in Lecture Notes in Computer Science, Springer, 2019, pp. 604–612, doi: 10.1007/978-3-030-32239-7_67.

R. H. J. M. Kurvers et al., “How to detect high-performing individuals and groups: Decision similarity predicts accuracy,” Science Advances, vol. 5, no. 11, Nov. 2019, doi: 10.1126/sciadv.aaw9011.

M. T. Shaban, C. Baur, N. Navab, and S. Albarqouni, “Staingan: Stain style transfer for digital histological images,” in Proceedings of the International Symposium on Biomedical Imaging, IEEE, Apr. 2019, pp. 953–956, doi: 10.1109/ISBI.2019.8759152.

J. G. Precious and S. Selvan, “Detection of Abnormalities in Ultrasound Images Using Texture and Shape Features,” in Proceedings of the 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), IEEE, Nov. 2018, doi: 10.1109/ICCTCT.2018.8551174.

B. Chaix, G. Delamon, A. Guillemassé, B. Brouard, and J. E. Bibault, “Psychological distress during the COVID-19 pandemic in France: A national assessment of at-risk populations,” General Psychiatry, vol. 33, no. 6, p. 100349, Nov. 2020, doi: 10.1136/gpsych-2020-100349.

R. H. J. M. Kurvers, S. M. Herzog, R. Hertwig, J. Krause, and M. Wolf, “Pooling decisions decreases variation in response bias and accuracy,” iScience, vol. 24, no. 7, p. 102740, 2021, doi: 10.1016/j.isci.2021.102740.

K. Zormpas-Petridis et al., “SuperHistopath: A Deep Learning Pipeline for Mapping Tumor Heterogeneity on Low-Resolution Whole-Slide Digital Histopathology Images,” Frontiers in Oncology, vol. 10, p. 3052, Jan. 2021, doi: 10.3389/fonc.2020.586292.

J. Z. Cheng et al., “Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans,” Scientific Reports, vol. 6, no. 1, pp. 1–13, Apr. 2016, doi: 10.1038/srep24454.

N. Dong et al., “Reinforced auto-zoom net: Towards accurate and fast breast cancer segmentation in whole-slide images,” in Lecture Notes in Computer Science, Springer, 2018, pp. 317–325, doi: 10.1007/978-3-030-00889-5_36.

U. R. Acharya et al., “A Novel Algorithm for Breast Lesion Detection Using Textons and Local Configuration Pattern Features with Ultrasound Imagery,” IEEE Access, vol. 7, pp. 22829–22842, 2019, doi: 10.1109/ACCESS.2019.2898121.

K. D. Miller et al., “Cancer statistics for adolescents and young adults, 2020,” CA: A Cancer Journal for Clinicians, vol. 70, no. 6, pp. 443–459, Nov. 2020, doi: 10.3322/caac.21637.

D. Wang, A. Khosla, R. Gargeya, H. Irshad, and A. H. Beck, “Deep Learning for Identifying Metastatic Breast Cancer,” Jun. 2016. [Online]. Available: https://arxiv.org/abs/1606.05718v1

W. Yue, Z. Wang, H. Chen, A. Payne, and X. Liu, “Machine learning with applications in breast cancer diagnosis and prognosis,” Designs, May 9, 2018, doi: 10.3390/designs2020013.

I. Carmichael et al., “Joint and Individual Analysis of Breast Cancer Histologic Images and Genomic Covariates,” Annals of Applied Statistics, vol. 15, no. 4, pp. 1697–1722, Dec. 2021, doi: 10.1214/20-AOAS1433.