###### Abstract  
Acute Lymphoblastic Leukemia (ALL) is a cancer that infects the blood cells causing the development of lymphocytes in large numbers. Diagnostic tests are costly and very time-consuming. It is important to diagnose ALL using Peripheral Blood Smear (PBS) images, especially in the initial screening cases. Several issues affect the examination process such as diagnostic error, symptoms, and nonspecific nature signs of ALL. Therefore, the objective of this study is to enforce machine-learning classifiers in the detection of Acute Lymphoblastic Leukemia as benign or malignant after using the grey wolf optimization algorithm in feature selection. The images have been enhanced by using an adaptive threshold to improve the contrast and remove errors. The model is based on grey wolf optimization technology which has been developed for feature reduction. Finally, acute lymphoblastic leukemia has been classified into benign and malignant using K-nearest neighbors (KNN), support vector machine (SVM), naive Bayes (NB), and random forest (RF) classifiers. The best accuracy, sensitivity, and specificity of this model were 99.69%, 99.5%, and 99%, respectively, after using the grey wolf optimization algorithm in feature selection. To ensure the effectiveness of the the proposed model, comparative results with other classification techniques have been included.  
20 September 2022 September  
Cancer is a group of rare, distinct, and lethal diseases. It can be characterized as uncontrolled cell growth with an abnormal pattern. The World Health Organization (WHO) estimates that 19.3 million people will receive a cancer diagnosis in 2020 and that there will be 1.6 times as many cancer deaths. The number of people affected is expected to be roughly 50% higher in 2040. According to WHO, the total number of ALL cases is 57,377, accounting for 21.9% of all childhood cancer cases worldwide in 2020 [3]. The most well-known leukemia classification system divides acute leukemia types into two groups according to the French-American-British model: acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL); the types of leukemia in the blood are shown in Figure 2. The most prevalent cancer types in children are thought to include ALL.  
Indeed, oncologists must identify the most important variables and factors that can be used to predict treatment outcomes. A further classification based on the French-American-British (FAB) classification divides ALL into three subtypes: L1, L2, and L3. L1 cells typically have a small, homogeneous size and little cytoplasm. These organisms have well-structured discoid nuclei. Compared with L1-type cells, L2-type cells are larger and have different shapes. Their cytoplasm is variable, and their nucleus is atypical. L3 cells have a round or oval nucleus and are all the same size and shape. The cytoplasm, which contains vacuoles, is sufficient in them. Usually, they are larger than L1 [3,4].  
To predict long-term outcomes or treatment outcomes, numerous studies have been carried out, and almost all of them have used data mining (DM) classifier algorithms, proving the effectiveness and significance of using machine learning techniques for such purposes.  
ALL subtypes have been categorized using World Health Organization (WHO) or French-American-British (FAB) techniques; hematologists and dermatologists have recently proposed that WHO categorization is superior to FAB categorization. To more accurately determine the different types of ALL, the WHO classified subgroups in more detail. Researchers from a variety of fields are now interested in using computer technologies such as artificial intelligence (AI) to diagnose blood diseases, particularly ALL. Concerning ALL diagnoses and classifications, the use of these technologies in the form of different algorithms has produced astounding results [5].  
Figure 1: Bone marrow and stem cells components.