An Improved Detection and Diagnosis Methods for Breast Tumor Detection System using IEMD Transform-based CNN Architecture

K.  Nagalakshmi; S.  Suriya; V.  Umadevi

Authors

K. Nagalakshmi Department of Computer Science and Engineering, Sethu Institute of Technology Virudhunagar, Tamilnadu, India
S. Suriya Department of Computer Science and Engineering, PSG College of Technology Coimbatore, Tamilnadu, India
V. Umadevi Department of Computer Science and Engineering, Arunai Engineering College, Thiruvannamalai, Tamilnadu, India

Keywords:

Mammogram; Breast tumor detection; IEMD Transform; CNN; Data augmentation.

Abstract

Mammogram is used to screen the tumor pixels in the breast portion of the human body. This paper develops a methodology for detecting and diagnosing the tumor regions in mammogram images using deep learning algorithm. The methodology consists of data augmentation, transform, and Convolutional Neural Networks (CNN) classifier with segmentation process for detecting the malignant mammogram image from the benign mammogram image. Then, the malignant mammogram image is analyzed for its severity levels using CNN classifier. The proposed system diagnosed the malignant case mammogram images into either Early or Advance using the developed CNN architecture. The simulation results of the proposed methods are compared with respect to other similar studies in this field.

Downloads

Download data is not yet available.

References

M. S Sayed., S. A Hassan., M. A Rashwan., M. I Abdalla. “Hybrid methods for feature extraction for breast masses classification”. Egyptian Informatics Journal, vol 19 no.1, pp 63–73. 2018.

B Sahiner., N Petrick., H. P Chan., et al. “Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization:. IEEE Transaction aon Medical Imaging,vol.20,no.12,pp.1275–1284, 2001.

M Abdel-Nasser., J Melendez., A Moreno., D Puig. “The impact of pixel resolution, integration scale, preprocessing, and feature normalization on texture analysis for mass classification in mammograms”, International Journal of Opticse.Vol. 3, no.3,pp.1–12,2012

R Nivetha., A Stephen Sagayaraj., G Mohanapriya., V Suganya., C Subhashini. “Detection of breast cancer using texture method in mammogram image”, International Journal of Innovative Research in Computer and Communication Engineering,Vol.5,no.3,pp.4780–4785, 2017

S. J. S Gardezi., A Elazab., B Lei., T. Wang, “Breast cancer detection and diagnosis using mammographicdata: systematic review”, Journal of Medical Internet Research.,Vol.21,no.7,pp. e14464, 2019.

P Palkar., P Agrawal. “A technique to extract statistical parameters of digital mammogram to detect breast cancer”,International Journal of Advanced Research in Science, Engineering and Technology.Vol.3,no.12,pp.3033–3038,2016.

Hassan Shayma’a., M. S Sayed., M. I Abdalla., M. A Rashwan. “Detection of breast cancer mass using mser A detector and features matching”, Multimedia Tools and Applications,Vol.78,no.14,pp.20239–20262,2019.

Anji Reddy, Vakaa Badal, Sonia Sudheer Reddy, “Breast cancer detection by leveraging Machine Learning” ,ICT Express, Vol. 6, no. 4, , Pages 320-324. December 2020.

Huang Qinghua, Chen Yongdong, Liu Longzhong, Tao Dacheng, Li Xuelong, “On combining biclustering mining and adaboost for breast tumor classification”, IEEE Trans. Knowl. Data Eng., vol.32,no.4 pp. 728-738,2020.

Saad Awadh Alanazi ,M. M. Kamruzzaman, Md Nazirul Islam Sarker, “Boosting Breast Cancer Detection Using Convolutional Neural Network” , Journal of Healthcare Engineering, Vol 2021, Article ID 5528622, pp.1-10,2021.

M. Masud, A. E. Eldin Rashed, and M. S. Hossain, “Convolutional neural network-based models for diagnosis of breast cancer,” Neural Computing and Applications, vol. 5, 2020.

J. L. Wang, A. K. Ibrahim, H. Zhuang, A. Muhamed Ali, A. Y. Li, and A. Wu, “A study on automatic detection of IDC breast cancer with convolutional neural networks,” in Proceedings of the 2018 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 703–708, Las Vegas, NV, USA, December 2018.

https://www.kaggle.com/skooch/ddsm-mammography.