Current Volume 9
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
Citations
IRE Journals:
N. Chinna Thambi, K. Aravind Kumar, S. Kalidass, Dr. M. Ilayaraja "AI Based Leukemia Detection Using Microscopic Blood Samples" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2308-2311 https://doi.org/10.64388/IREV9I11-1717891
IEEE:
N. Chinna Thambi, K. Aravind Kumar, S. Kalidass, Dr. M. Ilayaraja
"AI Based Leukemia Detection Using Microscopic Blood Samples" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717891
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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