A deep learning framework for leukemia cancer detection in microscopic blood samples using squeeze and excitation learning

M Bukhari, S Yasmin, S Sammad… - Mathematical …, 2022 - Wiley Online Library
Leukemia is a fatal category of cancer‐related disease that affects individuals of all ages,
including children and adults, and is a significant cause of death worldwide. Particularly, it is …

Red blood cell segmentation using masking and watershed algorithm: A preliminary study

JM Sharif, MF Miswan, MA Ngadi… - 2012 international …, 2012 - ieeexplore.ieee.org
Image segmentation is the most important step and a key technology in image processing
which directly affect the next processing. In human blood cell segmentation cases, many …

Convolutional neural networks for recognition of lymphoblast cell images

T Pansombut, S Wikaisuksakul… - Computational …, 2019 - Wiley Online Library
This paper presents the recognition for WHO classification of acute lymphoblastic leukaemia
(ALL) subtypes. The two ALL subtypes considered are T‐lymphoblastic leukaemia (pre‐T) …

Microscopic image segmentation and morphological characterization of novel chitosan/silica nanoparticle/nisin films using antimicrobial technique for blueberry …

R Sami, S Soltane, M Helal - Membranes, 2021 - mdpi.com
In the current work, the characterization of novel chitosan/silica nanoparticle/nisin films with
the addition of nisin as an antimicrobial technique for blueberry preservation during storage …

Automatic recognition of acute myelogenous leukemia in blood microscopic images using k-means clustering and support vector machine

F Kazemi, TA Najafabadi, BN Araabi - Journal of Medical Signals …, 2016 - journals.lww.com
Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is characterized
by the accumulation of myeloid blasts in the bone marrow. Careful microscopic examination …

A new acute leukaemia-automated classification system

S Agaian, M Madhukar… - Computer Methods in …, 2018 - Taylor & Francis
Acute leukaemia is a type of cancer that affects the blood and the bone marrow. Detection
and classification of white blood cells is a challenge in image processing, as manual data …

Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia.

NM Deshpande, S Gite, B Pradhan, K Kotecha… - Math Biosci …, 2022 - opus.lib.uts.edu.au
The diagnosis of leukemia involves the detection of the abnormal characteristics of blood
cells by a trained pathologist. Currently, this is done manually by observing the …

Detection of blood cancer in microscopic images of human blood samples: A review

M Saritha, BB Prakash, K Sukesh… - 2016 International …, 2016 - ieeexplore.ieee.org
For the fast and cost effective production of patient diagnosis, various image processing
techniques or software has been developed to get desired information from medical images …

Classification of blasts in acute leukemia blood samples using k-nearest neighbour

NZ Supardi, MY Mashor, NH Harun… - 2012 IEEE 8th …, 2012 - ieeexplore.ieee.org
The k-nearest neighbor (k-NN) is a traditional method and one of the simplest methods for
classification problems. Even so, results obtained through k-NN had been promising in …

[PDF][PDF] A comparative study of white blood cells segmentation using otsu threshold and watershed transformation

N Salem, NM Sobhy, M El Dosoky - Journal of Biomedical …, 2016 - researchgate.net
The aim of white blood cells (WBC) segmentation is to separate leukocytes from other
different components in the blood peripheral image. In this paper, a method to segment …