AIMC Topic: Leukemia
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Deep learning based semantic segmentation of leukemia effected white blood cell.
PloS one
Jan 1, 2025
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
Jul 1, 2023
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
Jun 29, 2022
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
Mar 14, 2022
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
Jan 1, 2022
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
May 10, 2020
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...
Fuzzy Gaussian Lasso clustering with application to cancer data.
Mathematical biosciences and engineering : MBE
Sep 30, 2019
Recently, Yang et al. (2019) proposed a fuzzy model-based Gaussian (F-MB-Gauss) clustering that combines a model-based Gaussian with fuzzy membership functions for clustering. In this paper, we further consider the F-MB-Gauss clustering with the leas...
Machine Learning Enables Live Label-Free Phenotypic Screening in Three Dimensions.
Assay and drug development technologies
Jan 1, 2018
There is a large amount of information in brightfield images that was previously inaccessible by using traditional microscopy techniques. This information can now be exploited by using machine-learning approaches for both image segmentation and the c...
A novel random forests-based feature selection method for microarray expression data analysis.
International journal of data mining and bioinformatics
Jan 1, 2015
High-dimensional data and a large number of redundancy features in bioinformatics research have created an urgent need for feature selection. In this paper, a novel random forests-based feature selection method is proposed that adopts the idea of str...
A genetic filter for cancer classification on gene expression data.
Bio-medical materials and engineering
Jan 1, 2015
We present a new genetic filter to identify a predictive gene subset for cancer-type classification on gene expression profiles. This approach pursues to not only maximize correlation between selected genes and cancer types but also minimize inter-co...
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