Datasets
Models
Cancer-test
Blood consists of plasma, and three different types of cells and they are: White Blood
Cells, Red Blood Cells and Platelets and each of these performs particular task. Red
blood cells transport oxygen from the lungs to the tissues of the body and vice versa.
White blood cells help the body to fight against diseases and infections. Platelets help
to clot and control bleeding. Leukemia is cancer of blood cells in which number of
white cells is increases numerously and those are immature cells that interfere with
other blood cells, usually red blood cells and platelets. Our body’s white blood cell ratio
is 1000:1. It means that between 1000 red blood cells there is 1 white blood cell.
There are two types of white blood cells that get turn into leukemia and they are:
1. Lymphoid cells
2. Myeloid cells
Leukemia that caused due to lymphoid cells is called lymphocytic or lymphoblastic
leukemia and if it is caused due to myeloid cells then it is known as myelogenous
or myeloid leukemia. Leukemia is grouped in two ways: acute or chronic, grouped
according to how fast the cells are growing. The abnormal blood cells in acute
leukemia are usually immature blasts (young cells) that do not work properly. These
cells are growing fast. Acute leukemia gets worse quickly unless it is immediately
treated. Young blood cells are present in chronic leukemia, but also mature
functional cells are produced. Blasts are growing slowly in chronic leukemia. It
takes the disease longer to get worse.
The four major forms of leukemia are:
1. Acute lymphoblastic leukemia (ALL)
2. Acute myelogenous leukemia (AML)
3. Chronic lymphocytic leukemia (CLL) and
4. Chronic myelogenous leukemia (CML)
PURPOSE
The purpose of our project is to develop a system that can automatically detect cancer
from the blood cell images. This system uses a convolution network that inputs a blood
cell images and outputs whether the cell is infected with cancer or not. The appearance
of cancer in blood cell images is often vague, can overlap with other diagnoses, and can
mimic many other benign abnormalities. These discrepancies cause considerable
variability among medical personnel in the diagnosis of cancer. Automated detection
of cancer from blood cell images at the level of expert medical personnel would not
only have tremendous benefit in clinical settings, it would also be invaluable in delivery
of health care to populations with inadequate access to diagnostic imaging specialists.