@article {10.3844/jcssp.2024.708.721,
article_type = {journal},
title = {Challenges of Breast Cancer Detection Based on Histopathology Images},
author = {Youssef, Alaa Mohamed and Youssif, Aliaa Abdel-Haleim Abdel-Razik and Behaidy, Wessam El},
volume = {20},
number = {7},
year = {2024},
month = {Apr},
pages = {708-721},
doi = {10.3844/jcssp.2024.708.721},
url = {https://thescipub.com/abstract/jcssp.2024.708.721},
abstract = {Artificial Intelligence (AI) is rapidly evolving every day to become increasingly potent and dependable. AI systems are becoming more sophisticated and are being utilized across various domains with the goal of enhancing human existence. Within the healthcare system, artificial intelligence finds application in handling and documenting large volumes of medical data, conducting analyses of healthcare systems, advancing pharmaceutical development, and aiding physicians in decision-making processes. Machines excel over humans in executing repetitive tasks consistently and reliably. In addition, the performance has recently been enhanced by the emersion of deep learning techniques. Breast cancer presents a significant danger to women globally as it reached 25.4% of new cases diagnosed with cancer types. Its danger increases with its ability to spread outside the breast through blood vessels and lymph vessels. The availability of histopathological images and the advancement in AI and machine learning techniques give new horizons for more investigation and studies of breast histopathology images. In this study, we demonstrate the different steps for detecting and classifying breast cancer through a journey from the preparation of breast tissue specimens to classification clarifying the different techniques used. Furthermore, we will discuss the challenges and solutions for histopathology images and the automated systems used.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}