Breast cancer detection using machine learning is an important and promising area of research and application in the field of healthcare. Machine learning algorithms have shown great potential in assisting medical professionals in early detection, diagnosis, and treatment planning for breast cancer
Breast cancer is a type of cancer that originates in the cells of the breast. It primarily affects women but can also occur in men, although it is much rarer in males. The cancer forms when the cells in the breast tissue begin to grow and divide uncontrollably, leading to the development of a tumor. This tumor can either be benign (non-cancerous) or malignant (cancerous).
Benign breast tumors do not spread to other parts of the body and are not considered cancer. They can still cause health issues and might need to be removed if they are causing pain, discomfort, or other problems. However, they do not pose the same level of threat as malignant tumors.
Malignant breast tumors are cancerous and have the potential to invade surrounding tissues and spread to other parts of the body through the lymphatic system or bloodstream. This process is known as metastasis and can make the cancer more difficult to treat and control. Breast cancer can be aggressive and life-threatening if not detected and treated early.
The aim of this project is to develop a robust and accurate machine learning model for the classification of breast cancer diagnoses into two categories: "malignant" and "benign." Breast cancer is a significant public health concern, and an effective classification model can assist healthcare professionals in making informed decisions about patient treatment and care.
Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall. There were over 2 million new cases in 2018.
Asia
Percentage of world population: 59 Percentage of new breast cancer cases: 39 Percentage of breast cancer deaths: 44
Africa
Percentage of world population: 15 Percentage of new breast cancer cases: 8 Percentage of breast cancer deaths: 12
U.S. and Canada
Percentage of world population: 5 Percentage of new breast cancer cases: 15 Percentage of breast cancer deaths: 9
The primary objective of this project is to create a predictive model that accurately distinguishes between malignant and benign breast cancer diagnoses based on relevant features extracted from medical imaging data and patient information.
By the end of this project, we anticipate having a highly accurate and efficient breast cancer classification model that can aid healthcare professionals in making informed decisions regarding patient diagnosis and treatment. The model's ability to distinguish between malignant and benign cases will contribute to improving patient care and outcomes.
The successful development and implementation of this breast cancer classification model have the potential to significantly impact the field of medical diagnostics. Accurate and timely identification of breast cancer types can lead to more effective treatment strategies, reduced medical costs, and improved patient survival rates.