ArticleComputational intelligence and neuroscience2021
Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 86 citations in OpenAlex.
- Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review).Oncology reports · 2026Review
- Improving acute lymphoblastic leukemia diagnosis through CBAM-enhanced VGG19 deep learning.Scientific reports · 2026Article
- BloodImage: Benchmarking vision transformers for blast detection in digital blood films using public and clinical datasets.Journal of pathology informatics · 2025Article
- Deep learning model for early acute lymphoblastic leukemia detection using microscopic images.Scientific reports · 2025Article
- An improved multiclass classification of acute lymphocytic leukemia using enhanced glowworm swarm optimization.Scientific reports · 2025Article
- ALL-Net: integrating CNN and explainable-AI for enhanced diagnosis and interpretation of acute lymphoblastic leukemia.PeerJ. Computer science · 2025Article
- A novel deep learning based approach with hyperparameter selection using grey wolf optimization for leukemia classification and hematologic malignancy detection.PeerJ. Computer science · 2025Article
- Utilizing Deep Feature Fusion for Automatic Leukemia Classification: An Internet of Medical Things-Enabled Deep Learning Framework.Sensors (Basel, Switzerland) · 2024Article
- A Chronological Overview of Using Deep Learning for Leukemia Detection: A Scoping Review.Cureus · 2024Article
- Advanced Data Processing of Pancreatic Cancer Data Integrating Ontologies and Machine Learning Techniques to Create Holistic Health Records.Sensors (Basel, Switzerland) · 2024Article
- Intra-nucleus mosaic pattern (InMop) and whole-cell Haralick combined-descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2023Article
- Morphological diagnosis of hematologic malignancy using feature fusion-based deep convolutional neural network.Scientific reports · 2023Article
- DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images.Diagnostics (Basel, Switzerland) · 2023Article
- Hybrid Feature-Learning-Based PSO-PCA Feature Engineering Approach for Blood Cancer Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features.Diagnostics (Basel, Switzerland) · 2023Article
- A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects.Journal of medical Internet research · 2022Review
- Biomedical Diagnosis of Leukemia Using a Deep Learner Classifier.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLeukemia is fatal cancer in both children and adults and is divided into acute and chronic. Acute lymphoblastic leukemia (ALL) is a subtype of this cancer. Early diagnosis of this disease can have a significant impact on the treatment of this disease. Computational intelligence-oriented techniques can be used to help physicians identify and classify ALL rapidly.
resultsThe validation accuracies (the mean accuracy of training and test networks for 100 training cycles) of the ResNet-50, VGG-16, and the proposed convolutional network were found to be 81.63%, 84.62%, and 82.10%, respectively. Among applied machine learning methods, the lowest obtained accuracy was related to multilayer perceptron (27.33%) and highest for random forest (81.72%).
conclusionThis study showed that the proposed convolutional neural network has optimal accuracy in the diagnosis of ALL. By comparing various convolutional neural networks and machine learning methods in diagnosing this disease, the convolutional neural network achieved good performance and optimal execution time without latency. This proposed network is less complex than the two pretrained networks and can be employed by pathologists and physicians in clinical systems for leukemia diagnosis.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.