Abstract:
Leukemia is a cancer-related disease which causes the death of individuals
worldwide, regardless of age and gender. It affects the blood and bone marrow, thus
leading to the abnormal production of immature white blood cells. Some of the factors
that might contribute to leukemia’s development might be related to genetics, radiation
or chemical exposure, infections, or immune system disorders. A reliable and fast
diagnosis of leukemia is crucial for a successful treatment to ensure high survival rates
and low number of deaths.
Nowadays, blood tests are widely used for diagnosing leukemia. Patients
undergo a complete blood count (CBC) to evaluate the count of blood cells present. In
cases of leukemia, CBC reveals abnormal count of white blood cells (WBC), red blood
cells (RBC) and platelets. Additionally, these blood cells are examined under a
microscope. Based on the results, immature or abnormal-looking white blood cells
may indicate leukemia. However, this type of diagnosis is often slow, time-consuming
and less accurate, mainly because under microscopes, the shape of leukemic cells
might seem similar to the shape of normal white cells, therefore making the diagnosis
prone to errors.
Therefore, in this thesis, we will focus on the deep learning algorithms which
have shown promising results in diagnosing leukemia cells. Some of these algorithms
include Convolutional Neural Networks (CNNs), which in the context of leukemia
cells diagnosis, can be trained to classify images of blood smears into normal blood cells or leukemic blood cells. The second algorithm includes Optimized Deep
Recurrent Neural Networks (ODRNNs), which can be used to analyze time-series data
such as videos of cell movements or changes in cell morphology over time. The last
algorithm is Transfer Learning, which is applied by fine-tuning a pre-trained neural
network on a dataset of leukemia cells. This approach helps improve the performance
of the model, especially when limited labelled data are available for training.
