### Related Studies  
Previous research efforts on leukemia patient classification will be reviewed in this section. Several researchers used microscopic images to propose leukemia detection methods. In [1], deep convolutional neural network (CNN) applications were investigated, and the diagnosis and classification of acute lymphoblastic leukemia, as well as the differentiation between cancer cases and healthy cases, were developed by the authors using pre-trained VGG-16 and ResNet. Different scenarios were designed for data analysis in [2]. The first scenario includes ALL patients, whereas the second scenario excludes patients who died from an unknown cause from the study. In general, common classification algorithms were used as SVM which showed a significantly improved performance in treatment outcome prediction. According to the experimental results in [3], CNN was used for automating the ALL-detection task from microscopic cell images. They looked at the weighted ensemble of various deep CNNs in the initial test set to make a better classification model for acute lymphoblastic leukemia cells. Using the microscopic blood image analysis [4], the authors segmented the image into the basic cell types, such as WBCs (white blood cells), RBCs (red blood cells), and platelets. The lymphocytes are then separated from the white blood cells. The changeable features such as the shape and color of lymphocytes were extracted with a support vector machine as a classifier, which classified the cells as normal or blasts. This detection system was found to be more effective, rapid, and accurate. In [6], Spark BigDL framework and Google Net architecture were suggested as detection techniques, and it achieved 97.33% accuracy. Aggregated deep learning for leukemic B-lymphoblast categorization was proposed, as shown in [7]. Data augmentation and transfer learning strategies were used to overcome  
Figure 2: Types of leukemia in blood. the limited dataset size and learning speed. The outcomes demonstrated that the suggested method outperformed individual networks in leukemic B-lymphoblast diagnosis by fusing features from the top deep learning models. A hybrid classification approach for extracting the WBC features was proposed in [8]. The algorithm relies on using a deep convolutional neural network (VGGNet) and filtering the resulting features with a statistically enhanced salp swarm algorithm (SESSA) to extract only relevant features and omit those that are not. The accuracy and complexity reduction of the suggested hybrid approach outperformed those of other approaches, which reduced computation time and resource usage. In [9], the researchers proposed a deep learning-based automated technique for distinguishing between malignant and benign cells. The use of microscopic blood cell images and CNN to diagnose ALL subtypes of leukemia was proposed by [10]. Several classification techniques were used such as naive Bayes, SVM, KNN, and decision trees. In [11], AlexNet, GoogleNet, and SqueezeNet were used in the proposed work. The objectives of these structures were to achieve robust, high-accuracy, faster, and more efficient methodology. In [12], a framework used CNNs. Cell images were used to train the model, extracting the best features after preprocessing the images. Then, the model was trained using the enhanced dense convolutional neural network framework (DCNN), and finally, the type of cancer cell type was predicted. A hybrid deep learning approach was proposed in [13], and they suggested a strategy for leukemic B-lymphoblast detection. Using convolutional neural networks and deep learning techniques, a method for classifying ALL into subtypes and reactive bone marrow (normal) in stained bone marrow images was proposed in [13]. This study's primary goal is to suggest a classification method for acute lymphoblastic leukemia. The technique is based on using grey wolf optimization as a features reduction algorithm.