AIMC Topic: Leukemia

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Leukaemia Stem Cells and Their Normal Stem Cell Counterparts Are Morphologically Distinguishable by Artificial Intelligence.

Journal of cellular and molecular medicine
Leukaemia stem cells (LSCs) are a rare population among the bulk of leukaemia cells and are responsible for disease initiation, progression/relapse and insensitivity to therapies in numerous haematologic malignancies. Identification of LSCs and monit...

Extracting Regions of Interest and Selective Feature Application in Leukaemia Image Classification.

Studies in health technology and informatics
Evaluating the blood smear test images remains the main route of detecting the type of leukaemia, accurate diagnosis is fundamental in providing effective treatment. The changes in the structure of the white blood cells present different morphologica...

Towards Diagnostic Intelligent Systems in Leukemia Detection and Classification: A Systematic Review and Meta-analysis.

Journal of evidence-based medicine
OBJECTIVE: Leukemia is a type of blood cancer that begins in the bone marrow and results in high numbers of abnormal white blood cells. Automated detection and classification of leukemia and its subtypes using artificial intelligence (AI) and machine...

An efficient leukemia prediction method using machine learning and deep learning with selected features.

PloS one
Leukemia is a serious problem affecting both children and adults, leading to death if left untreated. Leukemia is a kind of blood cancer described by the rapid proliferation of abnormal blood cells. An early, trustworthy, and precise identification o...

Deep learning based semantic segmentation of leukemia effected white blood cell.

PloS one
Medical image segmentation has numerous applications in diagnosing different diseases. Various types of diseases are found in white blood and Red blood cells. This paper represents the segmentation of WBCs from blood smear images. It is a complex and...

Automated Matching of Patients to Clinical Trials: A Patient-Centric Natural Language Processing Approach for Pediatric Leukemia.

JCO clinical cancer informatics
PURPOSE: Matching patients to clinical trials is cumbersome and costly. Attempts have been made to automate the matching process; however, most have used a trial-centric approach, which focuses on a single trial. In this study, we developed a patient...

A Comparison of Convolutional Neural Networks and Traditional Feature-Based Classification Applied to Leukaemia Image Analysis.

Studies in health technology and informatics
The accuracy of smear test image classification is a fundamental aspect in differentiating the type of leukaemia and determining the right treatment to improve the patient's chances of survival and recovery. Image Classification has lately become a v...

GSEnet: feature extraction of gene expression data and its application to Leukemia classification.

Mathematical biosciences and engineering : MBE
Gene expression data is highly dimensional. As disease-related genes account for only a tiny fraction, a deep learning model, namely GSEnet, is proposed to extract instructive features from gene expression data. This model consists of three modules, ...

Leukemia classification using the deep learning method of CNN.

Journal of X-ray science and technology
BACKGROUND: Processing Low-Intensity Medical Images (LI-MI) is difficult as outcomes are varied when it comes to manual examination, which is also a time-consuming process.

Expert knowledge for the recognition of leukemic cells.

Applied optics
This work shows the advantage of expert knowledge for leukemic cell recognition. In the medical area, visual analysis of microscopic images has regularly used biological samples to recognize hematological disorders. Nowadays, techniques of image reco...