Affiliations: [a] Department of Electronics, Sambalpur University, Odisha, India
| [b] Department of ET&T Engineering, Government Engineering College, Jagdalpur, CG, India
| [c] Department of Computer Science and Engineering, VSSUT, Odisha, India
| [d] Department of Computer Science & Engineering, St. Joseph’s Institute of Technology, India
| [e] Department of Computer Science and Engineering, SUIIT, Odisha, India
Correspondence:
[*]
Corresponding author. Chanki Pandey, Department of ET&T Engineering, Government Engineering College, Jagdalpur, CG, India. E-mail: chankipandeysri@gmail.com. and Prabira Kumar Sethy, Department of Electronics, Sambalpur University, Odisha, India. E-mail: prabirsethy.05@gmail.com.
Abstract: In the last decade, there have been extensive reports of world health organization (WHO) on breast cancer. About 2.1 million women are affected every year and it is the second most leading cause of cancer death in women. Initial detection and diagnosis of cancer appreciably increase the chance of saving lives and reduce treatment costs. In this paper, we perform a survey of the techniques utilized in breast cancer detection and diagnosis in image processing, machine learning (ML), and deep learning (DL). We also proposed a novel computer-vision based cost-effective method for breast cancer detection and diagnosis. Along with the detection and diagnosis of breast cancer, our proposed method is capable of finding the exact position of the abnormality present in the breast that will help in breast-conserving surgery or partial mastectomy. The proposed method is the simplest and cost-effective approach that has produced highly accurate and useful outcomes when compared with the existing approach.
Keywords: Breast cancer, computer vision, mammography, support vector machine (SVM), HOG features
