A Framework for Early Detection of Acute Lymphoblastic Leukemia and Its Subtypes From Peripheral Blood Smear Images Using Deep Ensemble Learning Technique
Acute lymphoblastic leukemia (ALL), one of the prevalent types of carcinogenic disease, has been seen a deadly illness exposing numerous patients across the world to potential threats of lives. It impacts both adults and children providing a narrow range of chances of being cured if diagnosed at a l...
Saved in:
Main Authors: | Sajida Perveen, Abdullah Alourani, Muhammad Shahbaz, M. Usman Ashraf, Isma Hamid |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10441810/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review
by: Mohammad Faiz, et al.
Published: (2024-07-01) -
ALL-Net: integrating CNN and explainable-AI for enhanced diagnosis and interpretation of acute lymphoblastic leukemia
by: Abhiram Thiriveedhi, et al.
Published: (2025-01-01) -
Incidence of secondary malignancies in Polish children treated for acute lymphoblastic leukemia according to the ALL-IC BFM 2002 protocol
by: Kamila Wypyszczak, et al.
Published: (2024-12-01) -
The combination of a tyrosine kinase inhibitor and blinatumomab in patients with Philadelphia chromosome–positive acute lymphoblastic leukemia or Philadelphia chromosome‐like acute lymphoblastic leukemia
by: Xiaoxia Wu, et al.
Published: (2024-09-01) -
A young adult patient with Philadelphia positive acute lymphoblastic leukemia presenting with extreme hyperleukocytosis
by: Gulten Tikit, et al.
Published: (2025-01-01)