Breast Cancer Detection and Diagnosis Using Machine Learning: A Survey
Riyadh M. Al-Tam, Sachin M. Narangale · Journal of scientific research · 2021
Breast cancer is one of the most widespread diseases causing death among women worldwide.Whenever a suspicion is raised, periodical exams usually including digital mammograms (DM), Infrared thermography, magnetic resonance imaging (MRI), ultrasound (US), microscopic (histological) images, microwave images, or other tools or tests might be recommended.Recently, many hardware and software have been applying different techniques for achieving high-quality results, especially the techniques of machine learning.In this paper, a comprehensive survey to review most of the accurate techniques being used for both detecting and diagnosing breast cancer is conducted.Besides, different commercial and non-commercial hardware and software are mentioned with their advantages and disadvantages in the process of detecting and diagnosing breast lesions.This study reveals that many techniques have been raised to help for breast cancer detection and diagnosis, however, there is no perfect modality that can detect and diagnose breast cancer alone.Moreover, a complete system that can deal with different modalities and gives 100% accuracy still a challenge, since the various structure of breast cancer and the different structure of images issued by a group of modalities that have been used.