# A Novel Deep Learning Segmentation and Classification Framework for Leukemia Diagnosis
## 1 Introduction  
Blood cancer affects children and adults [1]. Blood cancer is also known as leukemia and it occurs due to changes in white blood cells [1; 2]. Leukemia refers to the uncontrolled growth of blood cells [3]. Acute leukemia and chronic leukemia are considered the most common types of blood cancer that occur often and are widely diagnosed around the world [3; 4; 5]. Uncontrolled changes in white blood cells stimulate the birth of too many cells or generate unneeded behaviors [6]. Recently, physicians named four types of leukemia: acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and chronic myeloid leukemia (CML) [6; 7; 8; 9; 10; 11]. These types were categorized according to the intensity levels of tumor cells [12; 13; 14].  
Acute lymphoblastic leukemia (ALL) affects children and older adults (65 years old and over) [14; 15; 1]. Pathologists and physicians consider acute myeloid leukemia (AML) the most fatal type since just under 30% of patients have survived in the past five years [16; 1]. TheNational Cancer Institute in the United States of America reported that nearly 24,500 patients died in 2017 due to leukemia [1; 17; 18; 19; 20]. In addition, leukemia was the main cause of 4.1% reported cancers in the United States of America [1].  
Various known risk factors can cause leukemia. These factors include smoking, exposure to high levels of radiation and chemotherapy, a blood disorder, family history, and some genetic mutations [21; 22]. In young patients, leukemia occurs often due to genetic mutations that take place in blood cells only [23]. In addition, some genetic mutations can be inherited from parents and cause leukemia [24; 25].  
Acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and chronic myeloid leukemia (CML) are recognized by their intensity level and form of infected cells [1]. Acute myeloid leukemia (AML) occurs in adults more often than children, and more specifically, in men more often than women. In addition, acute myeloid leukemia (AML) is the deadliest type of leukemia since its five-year survival rate is 26.9% [1]. Chronic lymphocytic leukemia (CLL) affects men more than women, since two-thirds of the positive reported cases are men [1]. Moreover, this type of leukemia occurs in people aged 55 years and over, and its survival rate between 2007 and 2013 was 83.2% according to [1].  
The evaluation of leukemia is determined by expert pathologists who have sufficient skills [1]. Leukemia types are determined by Molecular Cytogenetics, Long-Distance Inverse Polymerase Chain Reaction (LDI-PCR), and Array-based Comparative Genomic Hybridization (ACGH) [1]. These procedures require substantial effort and skills [1]. Pathologists and physicians use smear blood and bone marrow tests to identify leukemia [1; 2; 3]. However, these tests are expensive and time-consuming. In addition, interventional radiology is another procedure used to identify leukemia [1]. Yet, the radiological procedures are limited by hereditary issues that are affected by the sensitivity and resolution of the imaging modality [1].  
At the moment, deep learning (DL) methods can be deployed in the medical field to support and provide aid for identifying infected leukemia cells or healthy ones. These approaches require datasets for training purposes. In this study, four datasets from Kaggle are used. Neural networks are widely used in image processing, detection, and categorization due to their rapid reputation for accuracy and effectiveness. Overfitting issues could occur for some reasons. To eliminate or minimize these issues, taking equal numbers of images from different samples is necessary. Various deep learning solutions for leukemia diagnosis and categorization were implemented with reasonable results. Therefore, obtaining greater accuracy is required.