A Novel Model for Breast Cancer Detection and Classification

Authors

  • N. Behar Department of Computer Science & Engineering, Guru Ghasidas University, India
  • M. Shrivastava Department of Computer Science & Engineering, Guru Ghasidas University, India
Volume: 12 | Issue: 6 | Pages: 9496-9502 | December 2022 | https://doi.org/10.48084/etasr.5115

Abstract

Breast cancer is a dreadful disease that affects women globally. The occurrences of masses in the breast region are the main cause of breast cancer development. It is important to detect breast cancer as early as possible as this might increase the survival rate. The existing research methodologies have the problems of increased computation complexity and low detection accuracy. To overcome such problems, this paper proposes an efficient breast cancer detection and classification system based on mammogram images. Initially, the mammogram images are preprocessed so unwanted regions and noise are removed and the contrast of the images is enhanced using Homo Morphic Adaptive Histogram Equalization (HMAHE). Then, the breast boundaries are identified with the use of the canny edge detector. After that, the pectoral muscles present in the images are detected and removed using the Global Pixel Intensity-based Thresholding (GPIT) method. Then, the tumors are identified and segmented by the Centroid-based Region Growing Segmentation (CRGS) algorithm. Next, the tumors are segmented and clustered and feature extraction is carried out from the clustered tumors. After that, the necessary features are selected by using the Chaotic Function-based Black Widow Optimization Algorithm (CBWOA). The selected features are utilized by the Convolutional Squared Deviation Neural Network Classifier (CSDNN) which classifies the tumors into six different categories. The proposed model effectively detects and classifies breast tumors and its efficiency is experimentally proved by comparison with the existing techniques.

Keywords:

classification, feature selection, Chaotic Function-based Black Widow Optimization Algorithm (CBWOA), Convolutional Squared Deviation Neural Network (CSDNN) classifier

Downloads

Download data is not yet available.

References

S. Ekici and H. Jawzal, "Breast cancer diagnosis using thermography and convolutional neural networks," Medical Hypotheses, vol. 137, Apr. 2020, Art. no. 109542. DOI: https://doi.org/10.1016/j.mehy.2019.109542

D. Singh and A. K. Singh, "Role of image thermography in early breast cancer detection- Past, present and future," Computer Methods and Programs in Biomedicine, vol. 183, Jan. 2020, Art. no. 105074. DOI: https://doi.org/10.1016/j.cmpb.2019.105074

M. Abdar and V. Makarenkov, "CWV-BANN-SVM ensemble learning classifier for an accurate diagnosis of breast cancer," Measurement, vol. 146, pp. 557–570, Nov. 2019. DOI: https://doi.org/10.1016/j.measurement.2019.05.022

D. A. Omondiagbe, S. Veeramani, and A. S. Sidhu, "Machine Learning Classification Techniques for Breast Cancer Diagnosis," IOP Conference Series: Materials Science and Engineering, vol. 495, Jun. 2019, Art. no. 012033. DOI: https://doi.org/10.1088/1757-899X/495/1/012033

P. Jasbi et al., "Breast cancer detection using targeted plasma metabolomics," Journal of Chromatography B, vol. 1105, pp. 26–37, Jan. 2019. DOI: https://doi.org/10.1016/j.jchromb.2018.11.029

M. Swellam, R. F. K. Zahran, H. Abo El-Sadat Taha, N. El-Khazragy, and C. Abdel-Malak, "Role of some circulating MiRNAs on breast cancer diagnosis," Archives of Physiology and Biochemistry, vol. 125, no. 5, pp. 456–464, Oct. 2019. DOI: https://doi.org/10.1080/13813455.2018.1482355

1482355.

N. Liu, E.-S. Qi, M. Xu, B. Gao, and G.-Q. Liu, "A novel intelligent classification model for breast cancer diagnosis," Information Processing & Management, vol. 56, no. 3, pp. 609–623, May 2019. DOI: https://doi.org/10.1016/j.ipm.2018.10.014

M. M. Rahman, Y. Ghasemi, E. Suley, Y. Zhou, S. Wang, and J. Rogers, "Machine Learning Based Computer Aided Diagnosis of Breast Cancer Utilizing Anthropometric and Clinical Features," IRBM, vol. 42, no. 4, pp. 215–226, Aug. 2021. DOI: https://doi.org/10.1016/j.irbm.2020.05.005

Ü. Budak, Z. Cömert, Z. N. Rashid, A. Şengür, and M. Çıbuk, "Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images," Applied Soft Computing, vol. 85, Dec. 2019, Art. no. 105765. DOI: https://doi.org/10.1016/j.asoc.2019.105765

R. S. Patil and N. Biradar, "Automated mammogram breast cancer detection using the optimized combination of convolutional and recurrent neural network," Evolutionary Intelligence, vol. 14, no. 4, pp. 1459–1474, Dec. 2021. DOI: https://doi.org/10.1007/s12065-020-00403-x

S. Dalwinder, S. Birmohan, and K. Manpreet, "Simultaneous feature weighting and parameter determination of Neural Networks using Ant Lion Optimization for the classification of breast cancer," Biocybernetics and Biomedical Engineering, vol. 40, no. 1, pp. 337–351, Jan. 2020. DOI: https://doi.org/10.1016/j.bbe.2019.12.004

D. Muduli, R. Dash, and B. Majhi, "Automated breast cancer detection in digital mammograms: A moth flame optimization based ELM approach," Biomedical Signal Processing and Control, vol. 59, May 2020, Art. no. 101912. DOI: https://doi.org/10.1016/j.bspc.2020.101912

C. Beltran-Perez, H.-L. Wei, and A. Rubio-Solis, "Generalized Multiscale RBF Networks and the DCT for Breast Cancer Detection," International Journal of Automation and Computing, vol. 17, no. 1, pp. 55–70, Feb. 2020. DOI: https://doi.org/10.1007/s11633-019-1210-y

T. A. Assegie, "An optimized K-Nearest Neighbor based breast cancer detection," Journal of Robotics and Control (JRC), vol. 2, no. 3, 2021. DOI: https://doi.org/10.18196/jrc.2363

H. Dhahri, E. Al Maghayreh, A. Mahmood, W. Elkilani, and M. Faisal Nagi, "Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms," Journal of Healthcare Engineering, vol. 2019, pp. 1–11, Nov. 2019. DOI: https://doi.org/10.1155/2019/4253641

H. N. Iqbal, A. B. Nassif, and I. Shahin, "Classifications of Breast Cancer Diagnosis using Machine Learning," International Journal of Computers, vol. 14, pp. 86–86, Dec. 2020. DOI: https://doi.org/10.46300/9108.2020.14.13

S. Chaudhury et al., "Effective Image Processing and Segmentation-Based Machine Learning Techniques for Diagnosis of Breast Cancer," Computational and Mathematical Methods in Medicine, vol. 2022, pp. 1–6, Apr. 2022. DOI: https://doi.org/10.1155/2022/6841334

R. Chauhan, P. K. Vinod, and C. V. Jawahar, "Exploring Genetic-histologic Relationships in Breast Cancer." arXiv, Mar. 14, 2021. DOI: https://doi.org/10.1109/ISBI48211.2021.9434130

A. Al-Gburi, N. Alwan, E. Al-Tameemi, and W. H.Al-Dabbagh, "Opportunistic Screening for Early Detection of Breast Cancer in Iraq Iraq," International Medical Journal, vol. 28, no. 1, pp. 28–32, Feb. 2021.

M. Monirujjaman Khan et al., "Machine Learning Based Comparative Analysis for Breast Cancer Prediction," Journal of Healthcare Engineering, vol. 2022, pp. 1–15, Apr. 2022. DOI: https://doi.org/10.1155/2022/4365855

S. Sharma, R. Mehra, and S. Kumar, "Optimised CNN in conjunction with efficient pooling strategy for the multi‐classification of breast cancer," IET Image Processing, vol. 15, no. 4, pp. 936–946, Mar. 2021. DOI: https://doi.org/10.1049/ipr2.12074

V. Chaurasia, M. Pandey, and S. Pal, "Prediction of Presence of Breast Cancer Disease in the Patient using Machine Learning Algorithms and SFS," IOP Conference Series: Materials Science and Engineering, vol. 1099, no. 1, Mar. 2021, Art. no. 012003. DOI: https://doi.org/10.1088/1757-899X/1099/1/012003

J. Quist, L. Taylor, J. Staaf, and A. Grigoriadis, "Random Forest Modelling of High-Dimensional Mixed-Type Data for Breast Cancer Classification," Cancers, vol. 13, no. 5, Feb. 2021, Art. no. 991. DOI: https://doi.org/10.3390/cancers13050991

A. B. S. Salamh and H. I. Akyüz, "A Novel Feature Extraction Descriptor for Face Recognition," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 8033–8038, Feb. 2022. DOI: https://doi.org/10.48084/etasr.4624

N. Kumar, A. Hashmi, M. Gupta, and A. Kundu, "Automatic Diagnosis of Covid-19 Related Pneumonia from CXR and CT-Scan Images," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 7993–7997, Feb. 2022. DOI: https://doi.org/10.48084/etasr.4613

N. K. Al-Shammari et al., "Cardiac Stroke Prediction Framework using Hybrid Optimization Algorithm under DNN," Engineering, Technology & Applied Science Research, vol. 11, no. 4, pp. 7436–7441, Aug. 2021. DOI: https://doi.org/10.48084/etasr.4277

A. Kehili, Κ. Dabbabi, and A. Cherif, "Early Detection of Parkinson’s and Alzheimer’s Diseases using the VOT_Mean Feature," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 6912–6918, Apr. 2021. DOI: https://doi.org/10.48084/etasr.4038

"MIAS Mammography," Kaggle. https://www.kaggle.com/datasets/kmader/mias-mammography.

M. Gupta, N. Kumar, N. Gupta, and A. Zaguia, "Fusion of Multi-Modality Biomedical Images Using Deep Neural Networks." Nov. 11, 2021. DOI: https://doi.org/10.21203/rs.3.rs-983420/v1

N. Kumar, N. Narayan Das, D. Gupta, K. Gupta, and J. Bindra, "Efficient Automated Disease Diagnosis Using Machine Learning Models," Journal of Healthcare Engineering, vol. 2021, pp. 1–13, May 2021. DOI: https://doi.org/10.1155/2021/9983652

Downloads

How to Cite

[1]
N. Behar and M. Shrivastava, “A Novel Model for Breast Cancer Detection and Classification”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 6, pp. 9496–9502, Dec. 2022.

Metrics

Abstract Views: 430
PDF Downloads: 334

Metrics Information
Bookmark and Share

Most read articles by the same author(s)