# An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
## 1 Introduction  
Acute lymphoblastic leukemia, generally known as ALL, is a type of blood cancer that usually begins in the bone marrow where the blood cells are formed. It is the type of cancer that is associated with white blood cells (WBC). Based on the age of the disease, leukemia is divided into four main types: chronic lymphocytic leukemia (CLL), chronic myeloid leukemia (CML), acute myelogenous leukemia (AML), and acute lymphoblastic leukemia (ALL) [1, 2]. In acute leukemia, the abnormal cells grow and spread rapidly and require immediate treatment, while chronic leukemia is hard to detect in its early stages. As a result, the blood cannot perform its normal function, making the immune system more susceptible. Moreover, in ALL bone marrow cannot produce healthy platelets and red blood cells, making most parts of the body vulnerable [3]. In ALL, bone marrow generates a large quantity of abnormal WBC. These WBC can stream into the blood and harm different parts of the body like the spleen, brain, kidney, and liver, which can lead to other dangerous types of cancer. Since ALL can spread quickly throughout the body, sometimes it can cause deaths if not treated or diagnosed in the earlystage. According to the key statistics about ALL, in 2021 more than 5690 cases of ALL are identified in the United States only [4] and out of which more than 1550 people are anticipated to die including both youngsters and adults. If leukemia, and especially ALL is diagnosed in its early stages, it is easily curable. The symptoms of leukemia are similar to other diseases like anemia, joint pain, fever, weakness, and bone pain, which is why the diagnosis of leukemia sometimes becomes difficult. There are a number of invasive methods which are usually used by hematologists to diagnose the disease. Generally, a biopsy is done which is an invasive method that is done by conducting tests on blood, bone marrow or spinal fluid [5]. These methods are painful, costly, and time-consuming. In these checkups, the medical specialist examines if the quantity of WBCs is sufficiently high and there are other relevant physical observations then it is highly likely the presence of ALL. These manual and expert-specific methods are also prone to error as the results of such methods are highly dependent on the knowledge and skills of the expert conducting the analysis [6]. To avoid the complexities involved with such invasive methods, and to provide faster, safe, and cost-effective solutions, medical image analysis-based techniques are employed. Image processing and computer vision-based methods are easy to generalize and eliminate the error of human factor. The image-based analysis can easily be done by radiologists. Such methods can provide benefit in a way that these are non-invasive however they do suffer from the same issues as invasive methods. The manual analysis by radiologists becomes burdensome, error-prone, and tremendously tedious when human experts are required to analyze the large datasets consisting of hundreds and thousands of medical images. Inherent overlapping features like morphology and texture in medical, especially histopathology images make the task harder. This fact is shown clearly in Figure 1, where images of ALL cancer and healthy cells are given. One can observe that these leukocytes are difficult to detect and classify due to their high intraclass homogeneity and low interclass separability. Consequently, developing accurate and reliable methodologies for leukemia recognition is important for timely diagnosis and early treatment. However, in general, ALL cancer cells differ from healthy cells on the basis of various factors including morphology, cell size, shape, and texture [7]. A computational classifier can use any of these distinguishing features to distinguish ALL cancer and healthy cell images. To remove the aforementioned issues associated with invasiveness and human experts, a fully automated solution based on convolutional neural networks is proposed in this work.