ReviewComputational and mathematical methods in medicine2018
Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia.
Review in Computational and mathematical methods in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Systematic Review of an Automated Multiclass Detection and Classification System for Acute Leukaemia in Terms of Evaluation and Benchmarking, Open Challenges, Issues and Methodological Aspects.Journal of medical systems · 2018Pooled it
- Privacy-preserving federated learning with light-weight attention improved CNNs for automated leukemia detection across distributed medical imaging.Scientific reports · 2026Article
- Hybrid neutrosophic enhanced MobileNetV2 model for leukemia blood cell classification.Frontiers in artificial intelligence · 2026Article
- Efficient blood cell classification from microscopic smear images using U-Net segmentation and a lightweight CNN.Scientific reports · 2025Article
- Advanced automated classification and segmentation of leukemic cells using simulated optical scanning holography and active contour methods.Journal of biomedical optics · 2025Article
- An explainable deep learning-based feature fusion model for acute lymphoblastic leukemia diagnosis and severity assessment.Frontiers in medicine · 2025Article
- Application of image recognition technology in pathological diagnosis of blood smears.Clinical and experimental medicine · 2024Review
- An attention-based deep learning for acute lymphoblastic leukemia classification.Scientific reports · 2024Article
- Blood cell image segmentation and classification: a systematic review.PeerJ. Computer science · 2024Article
- High-accuracy morphological identification of bone marrow cells using deep learning-based Morphogo system.Scientific reports · 2023Article
- Fuzzy Model for the Automatic Recognition of Human Dendritic Cells.Journal of imaging · 2023Article
- Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method.Computational intelligence and neuroscience · 2022Article
- Detection and Classification of Immature Leukocytes for Diagnosis of Acute Myeloid Leukemia Using Random Forest Algorithm.Bioengineering (Basel, Switzerland) · 2020Article
- Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network.Computational intelligence and neuroscience · 2020Article
- A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA.Scientific reports · 2019Article
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Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Leukaemia is a form of blood cancer which affects the white blood cells and damages the bone marrow. Usually complete blood count (CBC) and bone marrow aspiration are used to diagnose the acute lymphoblastic leukaemia. It can be a fatal disease if not diagnosed at the earlier stage. In practice, manual microscopic evaluation of stained sample slide is used for diagnosis of leukaemia. But manual diagnostic methods are time-consuming, less accurate, and prone to errors due to various human factors like stress, fatigue, and so forth. Therefore, different automated systems have been proposed to wrestle the glitches in the manual diagnostic methods. In recent past, some computer-aided leukaemia diagnosis methods are presented. These automated systems are fast, reliable, and accurate as compared to manual diagnosis methods. This paper presents review of computer-aided diagnosis systems regarding their methodologies that include enhancement, segmentation, feature extraction, classification, and accuracy.
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Registered trials
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.