Classification Of Acute Lymphoblastic Leukemia Research Articles

Detection and Classification of Acute Lymphoblastic Leukemia using CNN

Acute lymphoblastic leukemia is a very important cancer in childhood but quite prominent in later years of life for the genetic defects in lymphoid progenitors, which are hallmarks of the disease. In children, ALL mostly affects those aged between 2 and 6 years old and, against the background of contemporary biology knowledge and treatment approaches, is associated with more than 80% cure rates. However, approximately 20% of children with ALL relapse; therefore, there is a huge need for better risk identification and treatment optimization. In adults, ALL mainly affects B-cell precursors and is treated with chemotherapy and, in some cases, stem cell transplantation. An accurate and early diagnosis of ALL is of key importance but difficult to realize due to morphological similarities between normal cells and leukemic cells. This study, therefore, proposes a CNN model to improve diagnostic accuracy. Furthermore, it exploits the capabilities of CNN in feature extraction with Adamax Optimizer and the Categorical Cross-Entropy Loss Function to deal with imbalances and noise in the dataset. RESNET50-CNN has achieved 98.63% accuracy in classification and is hence a very strong tool in ALL detection and classification.

An attention-based deep learning for acute lymphoblastic leukemia classification

The bone marrow overproduces immature cells in the malignancy known as Acute Lymphoblastic Leukemia (ALL). In the United States, about 6500 occurrences of ALL are diagnosed each year in both children and adults, comprising nearly 25% of pediatric cancer cases. Recently, many computer-assisted diagnosis (CAD) systems have been proposed to aid hematologists in reducing workload, providing correct results, and managing enormous volumes of data. Traditional CAD systems rely on hematologists’ expertise, specialized features, and subject knowledge. Utilizing early detection of ALL can aid radiologists and doctors in making medical decisions. In this study, Deep Dilated Residual Convolutional Neural Network (DDRNet) is presented for the classification of blood cell images, focusing on eosinophils, lymphocytes, monocytes, and neutrophils. To tackle challenges like vanishing gradients and enhance feature extraction, the model incorporates Deep Residual Dilated Blocks (DRDB) for faster convergence. Conventional residual blocks are strategically placed between layers to preserve original information and extract general feature maps. Global and Local Feature Enhancement Blocks (GLFEB) balance weak contributions from shallow layers for improved feature normalization. The global feature from the initial convolution layer, when combined with GLFEB-processed features, reinforces classification representations. The Tanh function introduces non-linearity. A Channel and Spatial Attention Block (CSAB) is integrated into the neural network to emphasize or minimize specific feature channels, while fully connected layers transform the data. The use of a sigmoid activation function concentrates on relevant features for multiclass lymphoblastic leukemia classification The model was analyzed with Kaggle dataset (16,249 images) categorized into four classes, with a training and testing ratio of 80:20. Experimental results showed that DRDB, GLFEB and CSAB blocks’ feature discrimination ability boosted the DDRNet model F1 score to 0.96 with minimal computational complexity and optimum classification accuracy of 99.86% and 91.98% for training and testing data. The DDRNet model stands out from existing methods due to its high testing accuracy of 91.98%, F1 score of 0.96, minimal computational complexity, and enhanced feature discrimination ability. The strategic combination of these blocks (DRDB, GLFEB, and CSAB) are designed to address specific challenges in the classification process, leading to improved discrimination of features crucial for accurate multi-class blood cell image identification. Their effective integration within the model contributes to the superior performance of DDRNet.

An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.

In the field of medicine, decision support systems play a crucial role by harnessing cutting-edge technology and data analysis to assist doctors in disease diagnosis and treatment. Leukemia is a malignancy that emerges from the uncontrolled growth of immature white blood cells within the human body. An accurate and prompt diagnosis of leukemia is desired due to its swift progression to distant parts of the body. Acute lymphoblastic leukemia (ALL) is an aggressive type of leukemia that affects both children and adults. Computer vision-based identification of leukemia is challenging due to structural irregularities and morphological similarities of blood entities. Deep neural networks have shown promise in extracting valuable information from image datasets, but they have high computational costs due to their extensive feature sets. This work presents an efficient pipeline for binary and subtype classification of acute lymphoblastic leukemia. The proposed method first unveils a novel neighborhood pixel transformation method using differential evolution to improve the clarity and discriminability of blood cell images for better analysis. Next, a hybrid feature extraction approach is presented leveraging transfer learning from selected deep neural network models, InceptionV3 and DenseNet201, to extract comprehensive feature sets. To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. These optimized features subsequently empower multiple classifiers, potentially capturing diverse perspectives and amplifying classification accuracy. The proposed pipeline is validated on publicly available standard datasets of ALL images. For binary classification, the best average accuracy of 98.1% is achieved with 98.1% sensitivity and 98% precision. For ALL subtype classifications, the best accuracy of 98.14% was attained with 78.5% sensitivity and 98% precision. The proposed feature selection method shows a better convergence behavior as compared to classical population-based meta-heuristics. The suggested solution also demonstrates comparable or better performance in comparison to several existing techniques.

Evaluating Thresholding-Based Segmentation and Humoment Feature Extraction in Acute Lymphoblastic Leukemia Classification using Gaussian Naive Bayes

This study, titled "Evaluating Thresholding-Based Segmentation and HuMoment Feature Extraction in Acute Lymphoblastic Leukemia Classification using Gaussian Naive Bayes," investigates the application of image processing and machine learning techniques in the classification of Acute Lymphoblastic Leukemia (ALL). Utilizing a dataset of microscopic blood smear images, the research focuses on the efficacy of thresholding-based segmentation and Hu moment feature extraction in distinguishing between benign and malignant cases of ALL. Gaussian Naive Bayes, known for its simplicity and effectiveness, is employed as the classification algorithm. The study adopts a 5-fold cross-validation approach to evaluate the model's performance, with particular emphasis on metrics such as accuracy, precision, recall, and F1-score. Results indicate a high precision rate across all folds, averaging approximately 84.13%, while exhibiting variability in accuracy, recall, and F1-scores. These findings suggest that while the model is effective in identifying malignant cases, further refinements are necessary for improving overall accuracy and consistency. This research contributes to the field of medical image analysis by demonstrating the potential of combining simple yet efficient techniques for the automated diagnosis of hepatological diseases. It highlights the importance of integrating image processing with machine learning to enhance diagnostic accuracy in medical applications.

Classification of Acute Lymphoblastic Leukemia based on White Blood Cell Images using InceptionV3 Model

Acute lymphoblastic leukemia (ALL) is the most common form of leukemia that occurs in children. Detection of ALL through white blood cell image analysis can help with the prognosis and appropriate treatment. In this study, the author proposes an approach to classifying ALL based on white blood cell images using a convolutional neural network (CNN) model called InceptionV3. The dataset used in this research consists of white blood cell images collected from patients with ALL and healthy individuals. These images were obtained from The Cancer Imaging Archive (TCIA), which is a service that stores large-scale cancer medical images available to the public. During the evaluation phase, the author used training data evaluation metrics such as accuracy and loss to measure the model's performance. The research results show that the InceptionV3 model is capable of classifying white blood cell images with a high level of accuracy. This model achieves an average ALL recognition accuracy of 0.9896 with a loss of 0.031. The use of CNN models such as InceptionV3 in medical image analysis has the potential to improve the efficiency and precision of image-based disease diagnosis.

DeepLeukNet—A CNN based microscopy adaptation model for acute lymphoblastic leukemia classification

DeepLeukNet—A CNN based microscopy adaptation model for acute lymphoblastic leukemia classification

Emerging molecular subtypes and therapies in acute lymphoblastic leukemia.

Tremendous strides have been made in the molecular and cytogenetic classification of acute lymphoblastic leukemia based on gene expression profiling data, leading to an expansion of entities in the recent International Consensus Classification (ICC) of myeloid neoplasms and acute leukemias and 2022 WHO Classification of Tumours: Haematolymphoid Tumors, 5th edition. This increased diagnostic and therapeutic complexity can be overwhelming, and this review compares nomenclature differences between the ICC and WHO 5th edition publications, compiles key features of each entity, and provides a diagnostic algorithmic approach. In covering B-lymphoblastic leukemia (B-ALL), we divided the entities into established (those present in the revised 4th edition WHO) and novel (those added to either the ICC or WHO 5th edition) groups. The established B-ALL entities include B-ALL with BCR::ABL1 fusion, BCR::ABL1-like features, KMT2A rearrangement, ETV6::RUNX1 rearrangement, high hyperdiploidy, hypodiploidy (focusing on near haploid and low hypodiploid), IGH::IL3 rearrangement, TCF3::PBX1 rearrangement, and iAMP21. The novel B-ALL entities include B-ALL with MYC rearrangement; DUX4 rearrangement; MEF2D rearrangement; ZNF384 or ZNF362 rearrangement, NUTM1 rearrangement; HLF rearrangement; UBTF::ATXN7L3/PAN3,CDX2; mutated IKZF1 N159Y; mutated PAX5 P80R; ETV6::RUNX1-like features; PAX5 alteration; mutated ZEB2 (p.H1038R)/IGH::CEBPE; ZNF384 rearranged-like; KMT2A-rearranged-like; and CRLF2 rearrangement (non-Ph-like). Classification of T-ALL is complex with some variability in how the subtypes are defined in recent literature. It was classified as early T-precursor lymphoblastic leukemia/lymphoma and T-ALL, NOS in the WHO revised 4th edition and WHO 5th edition. The ICC added an entity into early T-cell precursor ALL, BCL11B-activated, and also added provisional entities subclassified based on transcription factor families that are aberrantly activated.

Abstract 2053: Structural variants detected by optical genome mapping in acute lymphoblastic leukemia patient-derived xenografts models

Abstract Background: The established classification of acute lymphoblastic leukemia (ALL) does not cover all high-risk patients because of the difficulty in detecting novel or rare structural variants (SVs). Furthermore, many SVs and formed gene fusions have adverse prognostic effects on hematological malignancies. This study aims to investigate SVs in 22 patient-derived xenografts (PDX) of ALL and to assess rare SVs and chromosomal aberrations, particularly hematological malignancy genes and gene fusions in different individuals detected by optical genome mapping (OGM). Methods: Leukemia cells from 16 patients with B-cell precursor acute lymphoblastic leukemia (B-ALL) and 6 patients with T-cell acute lymphoblastic leukemia (T-ALL) were inoculated into immuno-deficient mice, growth curves were monitored, and leukemia cells were collected after human CD45 was more than 80%. OGM (Bionano Genomics), RNA sequencing (RNA-seq) and whole genome sequencing (WGS) techniques were used to detect SVs and gene fusions. In validation, polymerase chain reaction (PCR) and fluorescence in situ hybridization (FISH) tests were performed to identify specific SVs. Results: On average, each model detected 42 rare SVs with known overlapping genes in different types by OGM: 12 insertions, 25 deletions, 1 inversion, 3 duplications, 2 inter-chromosomal translocations and 1 intra-chromosomal translocation. 5 rare SVs were found in multiple models (>=5), and two of them were never reported in B-ALL and T-ALL. Gene fusions were detected in all ALL models by OGM, and 17% were novel fusions. At the mRNA level, gene fusions were detected only in 19 models, and 24% of the predicted gene fusions by OGM were identical to the results of RNA-seq (high confidence results including pseudogenes). These two technologies could detect different fusion partners. For example, MLLT1 was seen at the mRNA level, and ACER1 was detected at the DNA level in the same KMT2A rearrangement model. Of the 16 B-ALL models, 6 were p190 BCR-ABL1 (a fusion of BCR exon 1 and ABL1 exon 2), 1 was p210 BCR-ABL1 (a fusion of BCR exon 13 and ABL1 exon 2) and 5 of 7 had IKZF1 gene deletions. 2 p190 BCR-ABL1 models were insensitive to imatinib and 2 p190 BCR-ABL1 models as well as the model with IKZF1 exons 4-7 deletion were sensitive to imatinib when treatment was initiated early in the disease. A novel ARL15-IKZF1 fusion was identified in one of the sensitive models. Deletion of the PRKAR2B gene was another rare SV in both models sensitive to imatinib besides the IKZF1 deletion. Conclusion: OGM assays were more comprehensive, and novel rare SVs were identified in ALL. Combining the results of gene fusions between different technologies will provide more accurate predictions for ALL classifications. The combination of aberrant SVs may synergize to influence the efficacy of imatinib, which warrants further investigations. Citation Format: Yueying Wang, Mengting Qin, JinPing Liu, Xiaobo Chen, Jia Xue, Sheng Guo. Structural variants detected by optical genome mapping in acute lymphoblastic leukemia patient-derived xenografts models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2053.

Classification of acute lymphoblastic leukemia using improved ANFIS

Classification of acute lymphoblastic leukemia using improved ANFIS

Optimized Deep Neuro-Fuzzy Network with MapReduce Architecture for Acute Lymphoblastic Leukemia Classification and Severity Analysis

The most common life-threatening disease, acute lymphoblastic leukemia (ALL), can be lethal within a few weeks if untreated. The early detection and analysis of leukemia is a key dilemma in the field of disease diagnosis, and the methods available for the classification process are time-consuming. To overcome the issues, this paper develops a robust classification technique named Horse Herd Whale Optimization-enabled Deep Neuro-Fuzzy Network (HHWO-enabled DNFN method) for ALL classification and severity analysis using the MapReduce framework. The input image is first preprocessed and segmented, and the useful features necessary for improving the classification performance are extracted during the mapper phase, known as HHWO, which incorporates Horse Herd Optimization Algorithm (HOA) and Whale Optimization Algorithm (WOA). Finally, severity analysis of ALL is done to classify the levels of leukemia to offer optimal treatment. As a result, the developed method performed better than other existing methods, achieving superior performance with a greater testing accuracy of 0.959, sensitivity of 0.965, and specificity of 0.966, respectively.

A new hybrid framework based on deep neural networks and JAYA optimization algorithm for feature selection using SVM applied to classification of acute lymphoblastic Leukaemia

ABSTRACT Identification of Acute lymphoblastic leukemia (ALL) in microscopic images is one of the most challenging tasks in medical image analysis. Despite a wide variety of image processing and deep learning techniques, the task of extracting features from ALL images and selecting the proper features from these redundant and high-dimensional feature space set, and then detecting ALL cells is still a complex issue. In this study, we present a new hybrid two-layer framework to construct an appropriate subset of features. In the first layer of the proposed method, image segmentation is implemented using an improved modified First-Spike-based approach integrated with a Gaussian function. Then a developed deep residual architecture is employed to extract the features. In the second layer, a powerful and reliable meta-heuristic algorithm known as the JAYA optimization algorithm is adopted for feature selection due to its computational efficiency. Finally, a support vector machine (SVM) is used to classify ALL images. To show the effectiveness of the proposed model, it is applied on microscopic images of blood samples from ALL images (ALL-IDB) and ISBI-2019 C-NMC dataset. The results show the superiority of the model to be an appropriate choice for future biomedical imaging tasks.

Detection and Classification of Acute Lymphoblastic Leukemia (ALL) from Blood Smear Microscopic Images

Abstract—White blood cells (WBC) which are also known as leukocytes, are a crucial part of the human body’s immune system. Leukaemia is a haematological disorder that starts from the bone marrow and affects white blood cells (WBCs) due to mutations in DNA causing blood cell production to become out of control. This prevents the development of healthy blood cells. Acute lymphocytic leukemia (ALL) is a kind of cancer where the bone marrow forms many more lymphocytes. ALL can further be classified into L1, L2 and L3 based on the structure of nucleus and cytoplasm. Automatic detection of leukemia or detection of blood cancer is a tough job and it is very much essential in healthcare centres. ALL identification and interpretation using peripheral blood smear (PBS) pictures plays an important role in early identification in screening and treatment. Conventionally, the method was achieved manually by a skilled technician during a considerable amount of time however the examination of those PBS pictures by laboratory users typically contains diagnostic errors owing to the nonspecific nature of ALL signs and symptoms that usually results in misdiagnosis. So, to achieve more accurate classification results we use powerful segmentation and deep learning techniques to train the model on these images. Firstly, we pre-process the images to apply segmentation methods on them. The proposed model will attempt to eradicate the probability of errors in the manual process and it can be used as a supporting analysis tool for pathologists. So, various Artificial Intelligence-based ALL classification methods and approaches are analysed in a well-defined manner with advantages and disadvantages. Keywords—Acute Lymphoblastic Leukemia, Classification, Detection, Deep Learning Techniques.

Entropy Measurements for Leukocytes' Surrounding Informativeness Evaluation for Acute Lymphoblastic Leukemia Classification.

The study of leukemia classification using deep learning techniques has been conducted by multiple research teams worldwide. Although deep convolutional neural networks achieved high quality of sick vs. healthy patient discrimination, their inherent lack of human interpretability of the decision-making process hinders the adoption of deep learning techniques in medicine. Research involving deep learning proved that distinguishing between healthy and sick patients using microscopic images of lymphocytes is possible. However, it could not provide information on the intermediate steps in the diagnosis process. As a result, despite numerous examinations, it is still unclear whether the lymphocyte is the only object in the microscopic picture containing leukemia-related information or if the leukocyte's surroundings also contain the desired information. In this work, entropy measures and machine learning models were applied to study the informativeness of both whole images and lymphocytes' surroundings alone for Leukemia classification. This work aims to provide human-interpretable features marking the probability of sickness occurrence. The research stated that the hue distribution of images with lymphocytes obfuscated alone is informative enough to facilitate 93.0% accuracy in healthy vs. sick classification. The research was conducted on the ALL-IDB2 dataset.

Classification of acute lymphoblastic leukaemia using machine learning algorithms

Acute Lymphoblastic Leukemia is a serious disease which may cause death if it is not detected at an early stage. It is common in children as well as adults. The detection of ALL is being done manually by examining the blood smear samples under a microscope. Manual blood testing has got several shortcomings such as it is slow and accuracy is also less. Generally, the inspection is done by an experienced pathologists and if there is any malformations the presence of lymphoblasts will be there. The accuracy of the diagnosis will be dependent on the experience of the operator. The Proposed research work aims in improving the diagnosis of ALL using Machine Learning Classifiers. Few classifiers haven been applied and compared on the segmented dataset images. The automated system can provide several advantages like it will minimize human intervention and it would provide more accurate results. In this research work EDES-SVM and EDSC-SVM have been used for classification. Experimental results obtained are then compared with the results of other machine learning classifiers such as SVM, ESVM, DSC-SVM, DES-SVM. From the experimental results it is analysed that the proposed method outperforms the conventional methods.

A novel Deep Learning Framework (DLF) for classification of Acute Lymphoblastic Leukemia

A novel Deep Learning Framework (DLF) for classification of Acute Lymphoblastic Leukemia

Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors

Leukemia is characterized by an abnormal proliferation of leukocytes in the bone marrow and blood, which is usually detected by pathologists using a microscope to examine a blood smear. Leukemia identification and diagnosis in advance are a trending topic in medical applications for decreasing the death toll of individuals with Acute Lymphoblastic Leukemia (ALL). It is critical to analyze the white blood cells for the identification of ALL for which the blood smear images are utilized. This paper discusses and presents a micro-pattern descriptor, called Local Directional Number Pattern along with Multi-scale Weber Local Descriptor for feature extraction task to determine cancerous and noncancerous blood cells. A balanced dataset with 260 blood smear images from the ALL-IDB2 dataset was used as training data. Consequently, a proposed model was constructed by applying different individual and combined feature extraction methods, and fed into the machine learning classifiers (Decision Tree, Ensemble, K-Nearest Neighbors, Naïve Bayes, and Random Forest) to determine cancerous and noncancerous blood cells. Experimental results indicate that the developed feature fusion technique assured a reasonable performance compared to other existing works with a testing average accuracy of 97.69 ± 1.83% using Ensemble classifier.

Optimizing a Deep Residual Neural Network with Genetic Algorithm for Acute Lymphoblastic Leukemia Classification.

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer worldwide, and it is characterized by the production of immature malignant cells in the bone marrow. Computer vision techniques provide automated analysis that can help specialists diagnose this disease. Microscopy image analysis is the most economical method for the initial screening of patients with ALL, but this task is subjective and time-consuming. In this study, we propose a hybrid model using a genetic algorithm (GA) and a residual convolutional neural network (CNN), ResNet-50V2, to predict ALL using microscopy images available in ALL-IDB dataset. However, accurate prediction requires suitable hyperparameters setup, and tuning these values manually still poses challenges. Hence, this paper uses GA to find the best hyperparameters that lead to the highest accuracy rate in the models. Also, we compare the performance of GA hyperparameter optimization with Random Search and Bayesian optimization methods. The results show that GA optimization improves the accuracy of the classifier, obtaining 98.46% in terms of accuracy. Additionally, our approach sheds new perspectives on identifying leukemia based on computer vision strategies, which could be an alternative for applications in a real-world scenario.

A Systematic Review on Recent Advancements in Deep and Machine Learning Based Detection and Classification of Acute Lymphoblastic Leukemia

Automatic Leukemia or blood cancer detection is a challenging job and is very much required in healthcare centers. It has a significant role in early diagnosis and treatment planning. Leukemia is a hematological disorder that starts from the bone marrow and affects white blood cells (WBCs). Microscopic analysis of WBCs is a preferred approach for an early detection of Leukemia since it is cost-effective and less painful. Very few literature reviews have been done to demonstrate a comprehensive analysis of deep and machine learning-based Acute Lymphoblastic Leukemia (ALL) detection. This article presents a systematic review of the recent advancements in this knowledge domain. Here, various artificial intelligence-based ALL detection approaches are analyzed in a systematic manner with merits and demits. The review of these schemes is conducted in a structured manner. For this purpose, segmentation schemes are broadly categorized into signal and image processing-based techniques, conventional machine learning-based techniques, and deep learning-based techniques. Conventional machine learning-based ALL classification approaches are categorized into supervised and unsupervised machine learning is presented. In addition, deep learning-based classification methods are categorized into Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and the Autoencoder. Then, CNN-based classification schemes are further categorized into conventional CNN, transfer learning, and other advancements in CNN. A brief discussion of these schemes and their importance in ALL classification are also presented. Moreover, a critical analysis is performed to present a clear idea about the recent research in this field. Finally, various challenging issues and future scopes are discussed that may assist readers in formulating new research problems in this domain.

IoT Application of Transfer Learning in Hybrid Artificial Intelligence Systems for Acute Lymphoblastic Leukemia Classification.

Acute lymphoblastic leukemia is the most common cancer in children, and its diagnosis mainly includes microscopic blood tests of the bone marrow. Therefore, there is a need for a correct classification of white blood cells. The approach developed in this article is based on an optimized and small IoT-friendly neural network architecture. The application of learning transfer in hybrid artificial intelligence systems is offered. The hybrid system consisted of a MobileNet v2 encoder pre-trained on the ImageNet dataset and machine learning algorithms performing the role of the head. These were the XGBoost, Random Forest, and Decision Tree algorithms. In this work, the average accuracy was over 90%, reaching 97.4%. This work proves that using hybrid artificial intelligence systems for tasks with a low computational complexity of the processing units demonstrates a high classification accuracy. The methods used in this study, confirmed by the promising results, can be an effective tool in diagnosing other blood diseases, facilitating the work of a network of medical institutions to carry out the correct treatment schedule.

An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification

Leukemia is a kind of blood cancer that influences people of all ages and is one of the leading causes of death worldwide. Acute lymphoblastic leukemia (ALL) is the most widely recognized type of leukemia found in the bone marrow of the human body. Traditional disease diagnostic techniques like blood and bone marrow examinations are slow and painful, resulting in the demand for non-invasive and fast methods. This work presents a non-invasive, convolutional neural network (CNN) based approach that utilizes medical images to perform the diagnosis task. The proposed solution consisting of a CNN-based model uses an attention module called Efficient Channel Attention (ECA) with the visual geometry group from oxford (VGG16) to extract better quality deep features from the image dataset, leading to better feature representation and better classification results. The proposed method shows that the ECA module helps to overcome morphological similarities between ALL cancer and healthy cell images. Various augmentation techniques are also employed to increase the quality and quantity of training data. We used the classification of normal vs. malignant cells (C-NMC) dataset and divided it into seven folds based on subject-level variability, which is usually ignored in previous methods. Experimental results show that our proposed CNN model can successfully extract deep features and achieved an accuracy of 91.1%. The obtained findings show that the proposed method may be utilized to diagnose ALL and would help pathologists.