Open Access Open Access  Restricted Access Subscription or Fee Access

A Review on Breast Cancer Diagnosis in Mammography Images Using Deep Learning Techniques

Naresh Khuriwal, Dr Nidhi Mishra


The Breast Cancer is second of the dangerous type diseases that is very effective for women in the world. Breast Cancer is spread very fast in last stage. It’s not give a more time for patients for treatment. We need accurate and efficient diagnosis system over a short period of time that can processed a large amount of data in a short time. It’s only possible using deep learning techniques. We are studying on a new diagnosis system for detecting Breast cancer in early stage. But in this paper we are describing the all techniques and images processing method for segmentation and filter images for breast cancer diagnosis. In this paper we have describe all algorithm and techniques for image pre-processing and post progressing. We have reviewed more than 20 papers from different publisher and find out that many researcher working on breast cancer diagnosis techniques. It’s a very latest and effective techniques for helping and saving the human life. In this paper we have also describe some experimental part of images segmentation and filter images with different filter method. It’s a very interesting area for researcher. In this paper we have used (Mammography Image Analysis Society) MIAS dataset having 95 images in .pgm format with 1024 X 1024 pixel. All paper divided in two main part first is data collection and image pre-processing. In this paper we have describe some most used deep learning pre-trained model that is very effective for images pre-processing. As per reviewed study the AlexNet, GoogleNet and VGG16 are provided best accuracy for detecting breast cancer in mammography images. We also used many filter method for image quality improvement. Filters are used to filter unwanted things or object in a spatial domain or surface. The main objective of the filter are to improve the quality of the images by enhancing and improve interoperability of the images. In this paper we also showing the comparison between most used pre-trained model that are used for breast cancer diagnosis.

Full Text:



J. Nagi, S. Abdul Kareem, F. Nagi and S. Khaleel Ahmed, "Automated breast profile segmentation for ROI detection using digital mammograms," 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, 2010, pp. 87-92.

F. A. Spanhol, L. S. Oliveira, C. Petitjean and L. Heutte, "Breast cancer histopathological image classification using Convolutional Neural Networks," 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, 2016, pp. 2560-2567.

Q. Pan, Y. Zhang, D. Chen and G. Xu, "Character-Based Convolutional Grid Neural Network for Breast Cancer Classification," 2017 International Conference on Green Informatics (ICGI), Fuzhou, 2017, pp. 41-48.

J. Wu, J. Shi, Y. Li, J. Suo and Q. Zhang, "Histopathological image classification using random binary hashing based PCANet and bilinear classifier," 2016 24th European Signal Processing Conference (EUSIPCO), Budapest, 2016, pp. 2050-2054.

D. Abdelhafiz, S. Nabavi, R. Ammar and C. Yang, "Survey

on deep convolutional neural networks in mammography," 2017 IEEE 7th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), Orlando, FL, 2017, pp. 1-1.

X. Zhang et al., "Whole mammogram image classification with convolutional neural networks," 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 700-704.

Z. Wu, J. Yuan, B. Lv and X. Zheng, "Digital mammography image enhancement using improved unsharp masking

approach," 2010 3rd International Congress on Image and

Signal Processing, Yantai, 2010, pp. 668-672.

M. Kallenberg et al., "Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring," in IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1322-1331, May 2016.

H. R. Mhaske and D. A. Phalke, "Melanoma skin cancer detection and classification based on supervised and unsupervised learning," 2013 International conference on Circuits, Controls and Communications (CCUBE), Bengaluru, 2013, pp. 1-5.

Y. Tsehay et al., "Biopsy-guided learning with deep convolutional neural networks for Prostate Cancer detection on multiparametric MRI," 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, 2017, pp. 642-645.

R. Shimizu et al., "Deep learning application trial to lung cancer diagnosis for medical sensor systems," 2016 International SoC Design Conference (ISOCC), Jeju, 2016,

pp. 191-192.

S. K. Wajid, A. Hussain, K. Huang and W. Boulila, "Lung cancer detection using Local Energy-based Shape Histogram (LESH) feature extraction and cognitive machine learning techniques," 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Palo Alto, CA, 2016, pp. 359-366.

M. R. Al-Hadidi, A. Alarabeyyat and M. Alhanahnah, "Breast Cancer Detection Using K-Nearest Neighbor Machine Learning Algorithm," 2016 9th International Conference on Developments in eSystems Engineering (DeSE), Liverpool, 2016, pp. 35-39.

A. Qasem et al., "Breast cancer mass localization based on machine learning," 2014 IEEE 10th International Colloquium on Signal Processing and its Applications, Kuala Lumpur, 2014, pp. 31-36.

A. Osareh and B. Shadgar, "Machine learning techniques to diagnose breast cancer," 2010 5th International Symposium on Health Informatics and Bioinformatics, Antalya, 2010,

pp. 114-120.


  • There are currently no refbacks.

This site has been shifted to