Development a BCIS for Breast Cancer classification, detection and segmentation.

Breast cancer stands as a formidable non-communicable disease, casting a somber shadow with its significant global mortality rates. According to the World Health Organization (WHO), breast cancer ranks as the most prevalent malignancy across the globe. In 2020 alone, there were 2.3 million new breast cancer diagnoses and 685,000 deaths. Yet, amidst this daunting challenge, there is a glimmer of hope. High-income countries have managed to curtail breast cancer mortality by a remarkable 40% since the 1980s, primarily through the implementation of routine mammography screening for at-risk populations. During the specified period, breast cancer jumped by 55% to constitute 29% of all cancer cases among Saudi females in 2020. With this backdrop, this project endeavors to pioneer a Breast Cancer Identifier System (BCIS) for the detection, classification, segmentation and Prognostic of breast cancer using various medical imaging modalities. This proposal lays the groundwork for exploring novel quantitative biomarkers and distinctive features of breast cancer through cutting-edge image processing techniques, with the goal of pinpointing and forecasting regions harboring malignant masses. A robust amalgamation of machine learning (ML) methodologies, particularly deep learning (DL) systems, XAI and artificial intelligence (AI) strategies will be devised to ascertain the subtypes and stages of breast cancer. Furthermore, the project will delve into identifying prognostic factors for breast cancer to enhance the tailored monitoring of patients before and after treatment. Diverse ultrasound imaging technologies, encompassing mammography and MRI image, will be harnessed for data acquisition. The efficacy of all developed algorithms will undergo rigorous validation. Ultimately, the culmination of this research will yield a sophisticated BCIS replete with a framework and user-friendly graphical interface primed for clinical integration. The BCIS model's quality will be thoroughly scrutinized using a diverse cohort of patients. The outcome of the proposed research might decrease the death rate by increasing the awareness of breast disease.