Breast Cancer Detection: Challenges and Future Research Developments

Breast cancer is the most hazardous diseases and it is the most common type of cancer. The leading type of cancer to be diagnosed for women is breast cancer. The survival rate of women is highly enhanced with the utilization of Breast cancer diagnosis models and takes proper treatment for breast cancer. The primary therapy concentrates on the surveillance of breast cancer but the early recognition of brain tumors are still challenging. The human interpretation is the critical issue while processing breast cancer recognition with the introduction of Computer Assisted Detection or Diagnosis (CAD). For the treatment and diagnosis of breast cancer, the Artificial intelligence, and several computer vision-based approaches are developed on the basis of machine and deep learning. Thus, this paper aims to survey the different detection methodologies for diagnosing and treating the breast cancer. It is also explore an extensive range of existing works with respect to various detection algorithms and compared against conventional techniques. This systematic review provides the detailed information to motivate the CAD for identifying the breast cancer effectively with the aid of clinical data. Further, the review work comprises with background knowledge of breast cancer detection in medical industry, methods for detection, different datasets for consideration, inclusive of various performance metrics, implemented tools they have taken and also explores the issues and challenges to provoke the further enhancement in future trends.