Aiding the Digital Mammogram for Detecting the Breast Cancer Using Shearlet Transform and Neural Network

P, Shenbagavalli; R, Thangarajan

doi:10.22034/APJCP.2018.19.9.2665

Aiding the Digital Mammogram for Detecting the Breast Cancer Using Shearlet Transform and Neural Network

Document Type : Research Articles

Authors

Shenbagavalli P ¹
Thangarajan R ²

¹ Department of Computer Science and Engineering, M.P.Nachimuthu M.Jaganathan Engineering College, Chennimalai, Erode-638 112, Tamilnadu, India.

² Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Tamilnadu, India.

10.22034/APJCP.2018.19.9.2665

Abstract

Objective: Breast Cancer is the most invasive disease and fatal disease next to lung cancer in human. Early detection
of breast cancer is accomplished by X-ray mammography. Mammography is the most effective and efficient technique
used for detection of breast cancer in women and also to improve the breast cancer prognosis. The numbers of images
need to be examined by the radiologists, the resulting may be misdiagnosis due to human errors by visual Fatigue.
In order to avoid human errors, Computer Aided Diagnosis is implemented. In Computer Aided Diagnosis system,
number of processing and analysis of an image is done by the suitable algorithm. Methods: This paper proposed a
technique to aid radiologist to diagnosis breast cancer using Shearlet transform image enhancement method. Similar to
wavelet filter, Shearlet coefficients are more directional sensitive than wavelet filters which helps detecting the cancer
cells particularly for small contours. After enhancement of an image, segmentation algorithm is applied to identify the
suspicious region. Result: Many features are extracted and utilized to classify the mammographic images into harmful
or harmless tissues using neural network classifier. Conclusions: Multi-scale Shearlet transform because more details on
data phase, directionality and shift invariance than wavelet based transforms. The proposed Shearlet transform gives multi resolution result and generate malign and benign classification more accurate up to 93.45% utilizing DDSM database.

Keywords