A review on traditional machine learning and deep learning models for WBCs classification in blood smear images

S Khan, M Sajjad, T Hussain, A Ullah, AS Imran - Ieee Access, 2020 - ieeexplore.ieee.org
In computer vision, traditional machine learning (TML) and deep learning (DL) methods
have significantly contributed to the advancements of medical image analysis (MIA) by …

Detection and classification of immature leukocytes for diagnosis of acute myeloid leukemia using random forest algorithm

S Dasariraju, M Huo, S McCalla - Bioengineering, 2020 - mdpi.com
Acute myeloid leukemia (AML) is a fatal blood cancer that progresses rapidly and hinders
the function of blood cells and the immune system. The current AML diagnostic method, a …

[PDF][PDF] Implementasi K-Nearest Neighbor untuk Klasifikasi Bunga Dengan Ekstraksi Fitur Warna RGB

L Farokhah - Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 2020 - academia.edu
Era computer vision merupakan era dimana komputer dilatih untuk bisa melihat,
mengidentifikasi dan mengklasifikasi seperti kecerdasan manusia. Algoritma klasifikasi …

Classification of atypical white blood cells in acute myeloid leukemia using a two-stage hybrid model based on deep convolutional autoencoder and deep …

TA Elhassan, MS Mohd Rahim, MH Siti Zaiton… - Diagnostics, 2023 - mdpi.com
Recent advancements in artificial intelligence (AI) have led to numerous medical
discoveries. The field of computer vision (CV) for medical diagnosis has received particular …

[HTML][HTML] An efficient algorithm for detection of white blood cell nuclei using adaptive three stage PCA-based fusion

M Makem, A Tiedeu - Informatics in Medicine Unlocked, 2020 - Elsevier
Morphological analysis of white blood cells under a microscope is a crucial laboratory
procedure for the diagnosis of several diseases including leukemia. This process is …

Utilization of K-nearest neighbor algorithm for classification of white blood cells in AML M4, M5, and M7

NPT Prakisya, F Liantoni, P Hatta… - Open …, 2021 - degruyter.com
Acute myeloid leukemia (AML) M4, M5, and M7 are subtypes of leukemia derived from
myeloid cell derivatives that influences the results of the identification of AMLs, which …

Image segmentation of acute myeloid leukemia using multi otsu thresholding

E Suryani, EI Asmari, B Harjito - Journal of Physics: Conference …, 2021 - iopscience.iop.org
Abstract Acute Myeloid Leukemia (AML) can be identified by utilizing image processing. The
stages used in the image processing process include preprocessing, segmentation, and …

CAE-ResVGG FusionNet: A Feature Extraction Framework Integrating Convolutional Autoencoders and Transfer Learning for Immature White Blood Cells in Acute …

T Elhassan, AH Osman, MSM Rahim, SZM Hashim… - Heliyon, 2024 - cell.com
Acute myeloid leukemia (AML) is a highly aggressive cancer form that affects myeloid cells,
leading to the excessive growth of immature white blood cells (WBCs) in both bone marrow …

Blood cells classification for identification of acute lymphoblastic leukemia on microscopic images using image processing

SO Heriawati, T Harsono, MM Bachtiar… - 2021 International …, 2021 - ieeexplore.ieee.org
Acute lymphoblastic leukemia (ALL) is a type of leukemia (cancer of the white blood cells)
that generally occurs in children. ALL have 3 sub-types, namely Ll, L2, and L3. Microscopic …

Classification of blast cell type on acute myeloid leukemia (AML) based on image morphology of white blood cells

W Wiharto, E Suryani, YR Putra - … Computing Electronics and …, 2019 - telkomnika.uad.ac.id
AML is one type of cancer of the blood and spinal cord. AML has a number of subtypes
including M0 and M1. Both subtypes are distinguished by the dominant blast cell type in the …