Breast Cancer Detection Using Machine Learning
Cancer occurs when changes called mutations take place in genes that regulate cell growth. The mutations let the cells divide and multiply in an uncontrolled, chaotic way. The cells keep on proliferating, producing copies that get progressively more abnormal. In most cases, the cell copies eventually end up forming a tumor.
Breast cancer occurs when a malignant (cancerous) tumor originates in the breast. As breast cancer tumors mature, they may metastasize (spread) to other parts of the body. The primary route of metastasis is the lymphatic system which, ironically enough, is also the body's primary system for producing and transporting white blood cells and other cancer-fighting immune system cells throughout the body. Metastasized cancer cells that aren't destroyed by the lymphatic system's white blood cells move through the lymphatic vessels and settle in remote body locations, forming new tumors and perpetuating the disease process.
Breast cancer is not just a woman's disease. It is quite possible for men to get breast cancer, although it occurs less frequently in men than in women. Our discussion will focus primarily on breast cancer as it relates to women but it should be noted that much of the information is also applicable for men.
A mammogram is an x-ray picture of the breast. It can be used to check for breast cancer in women who have no signs or symptoms of the disease. It can also be used if you have a lump or other sign of breast cancer.
Screening mammography is the type of mammogram that checks you when you have no symptoms. It can help reduce the number of deaths from breast cancer among women ages 40 to 70. But it can also have drawbacks. Mammograms can sometimes find something that looks abnormal but isn't cancer. This leads to further testing and can cause you anxiety. Sometimes mammograms can miss cancer when it is there. It also exposes you to radiation. You should talk to your doctor about the benefits and drawbacks of mammograms. Together, you can decide when to start and how often to have a mammogram.
Now while its difficult to figure out for physicians by seeing only images of x-ray that weather the tumor is toxic or not training a machine learning model according to the identification of tumour can be of great help.
To get more accuracy, we trained all supervised classification algorithms but you can try out a few of them which are always popular. After training all algorithms, we found that Logistic Regression, Random Forest and XGBoost classifiers are given high accuracy than remain but we have chosen XGBoost with accuracy of 98% approximately.
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