Home / Current Issue / Paper 1717891
AI Based Leukemia Detection Using Microscopic Blood Samples
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
DOI: https://doi.org/10.64388/IREV9I11-1717891
Abstract
The Cancer Cell Detection System was designed to accurately and efficiently identify cancer cells in blood samples. It uses techniques like machine learning to achieve this. The device uses a microscope to see pictures of blood. It improves them by eliminating noise, boosting contrast, and fragmenting the images. The system examines the size, shape, and texture of the cells following picture enhancement. Then, it uses machine learning models such as Logistic Regression, Support Vector Machine, and Convolutional Neural Networks to predict if a cell is cancerous or not. When it comes to automatically identifying patterns in photos, Convolutional Neural Networks excel. If configured correctly, logistic regression can also be effective. The Cancer Cell Detection System is intended to effectively and precisely identify cancer cells from blood samples. It uses methods like machine learning to do this. The device uses a microscope to examine pictures of blood. It improves them by dividing the images, increasing contrast, and eliminating noise. Following image enhancement, the system examines the cells' size, shape, and texture. Then, it uses machine learning models like Logistic Regression, Support Vector Machine, and Convolutional Neural Networks to identify whether a cell is cancerous or not. Convolutional neural networks are especially a dept at this and have the ability to automatically identify patterns in pictures. When configured correctly, logistic regression can also be effective.
Keywords
Logistic Regression, Blood Cell Categorization, Machine Learning, Convolutional Neural Networks, And Cancer Detection
References
[1] "Automatic Detection of Leukemia Using Microscopic Images with CNN", A. Rehman, S. A. Khan, M. Abbas, T. Saba, Computers in Biology and Medicine, 2018.
[2] "Deep Learning for Identification of Acute Lymphoblastic Leukemia in Blood Smear Images", C. Mohapatra, R. Behera, M. P. Singh, IEEE Access, 2021.
[3] "Image Analysis-Based System for Leukemia Detection Using Color and Texture Features", D. Putzu , G. Caocci , G.D. Giacomo, Computers in Biology and Medicine, 2014.
[4] "A Survey on Microscopic Image Analysis for Leukemia Detection", A. K. Sahu, B. Bhowmick, Procedia Computer Science, 2020.
[5] X. Wang, Journal of Healthcare Engineering, 2020.
[6] "Leukemia Detection Using Image Processing and Machine Learning", K. V. Ramya, S. U. Rehman, International Journal of Computer Applications, 2019.
[7] "AI-Driven Blood Smear Analysis for Early Diagnosis of Leukemia", P. Sharma, N. Yadav, Biomedical Engineering Letters, 2021.
[8] X. Wang, Journal of Healthcare Engineering, 2020.
[9] "Leukemia Detection Using Image Processing and Machine Learning", K. V. Ramya, S. U. Rehman, International Journal of Computer Applications, 2019.
[10] "Transfer Learning Approach for Classification of Leukemia Types in Microscopic Images", R. Joshi, M.R. Paul, Expert Systems with Applications,2023.
How to cite this paper
@article{1717891,
author = {N. Chinna Thambi, K. Aravind Kumar, S. Kalidass, Dr. M. Ilayaraja},
title = {AI Based Leukemia Detection Using Microscopic Blood Samples},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {2308-2311},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717891.pdf},
abstract = {The Cancer Cell Detection System was designed to accurately and efficiently identify cancer cells in blood samples. It uses techniques like machine learning to achieve this. The device uses a microscope to see pictures of blood. It improves them by eliminating noise, boosting contrast, and fragmenting the images. The system examines the size, shape, and texture of the cells following picture enhancement. Then, it uses machine learning models such as Logistic Regression, Support Vector Machine, and Convolutional Neural Networks to predict if a cell is cancerous or not. When it comes to automatically identifying patterns in photos, Convolutional Neural Networks excel. If configured correctly, logistic regression can also be effective. The Cancer Cell Detection System is intended to effectively and precisely identify cancer cells from blood samples. It uses methods like machine learning to do this. The device uses a microscope to examine pictures of blood. It improves them by dividing the images, increasing contrast, and eliminating noise. Following image enhancement, the system examines the cells' size, shape, and texture. Then, it uses machine learning models like Logistic Regression, Support Vector Machine, and Convolutional Neural Networks to identify whether a cell is cancerous or not. Convolutional neural networks are especially a dept at this and have the ability to automatically identify patterns in pictures. When configured correctly, logistic regression can also be effective.},
keywords = {Logistic Regression, Blood Cell Categorization, Machine Learning, Convolutional Neural Networks, And Cancer Detection},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717891}
}