Automatic Detection of White Blood Cancer and Lung Cancer Using Machine Learning

Abstract

Abstract: When abnormal white blood cells multiply cancerously, leukaemia results, harming the bone marrow and blood. The body's immune system is disturbed by the body's excessive generation of immature white blood cells, or lymphoblast, and its capacity to create red blood cells and platelets is decreased in the bone marrow as a result. Early diagnosis of these cells is crucial for preventing health issues in patients. In the past, the procedure was manually completed by an expert in a sizable amount of time. On a short dataset, the proposed model uses an improved Dense Convolutional neural network framework to predict the kind of cancer in a generic manner. which is able to locate both lung cancer and leukemia. Tumors can spread to other organs and grow quickly in lung cancer. An aberrant cell proliferation that can harm other normal tissue cells marks the beginning of cancer. Using tiny images to identify the white blood cancer, the imaging technology known as computerized tomography (CT) is frequently used to diagnose lung cancer.