Imaging & Machine Learning Techniques Used for Early Identification of Cancer in Breast Mammogram
Sushreeta Tripathy1, Tripti Swarnkar2

1Sushreeta Tripathy, Department of Computer Science & Information Technology, S’O’A Deemed to be University, Bhubaneswar, India.
2Tripti Swarnkar, Department of Computer Application, S’O’A Deemed to be University, Bhubaneswar, India. 

Manuscript received on 05 August 2019. | Revised Manuscript received on 12 August 2019. | Manuscript published on 30 September 2019. | PP: 7376-7383 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6140098319/2019©BEIESP | DOI: 10.35940/ijrte.C6140.098319
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Breast cancer has become a major concern of women health throughout the world and has an important cause of death among women. The important radiographic signs of cancer are the masses visible in the breast. In the initial stage, the masses in the women breast are very strenuous to detect. In many cases, it has been proven that a manual attempt of treatment methods are time consuming and inefficient. Hence there is a basic demand for well-planned methods for diagnosis of the cancerous cells with minimal human participation resulting high in precision. Mammography has been proven as an efficient technique for the identification of cancer in women breast. Automated detection of masses in breast mammogram is the major goal in the identification of cancer in women breast. Machine learning techniques can be used as an effective mechanism by the physician for the early detection of cancer in the breast. By early recognition of malignancy in the breast, patients will get treatment right from the initial stage of cancer which can save their lives.
Keywords: Breast Cancer, Computer Aided Detection (CAD), Mammogram, Machine Learning (ML), Region of Interest (ROI) .

Scope of the Article: Machine Learning