Archive

  • A Step Toward the Future: Using Machine Learning to Detect Leukemia
  • Chandigarh University, Gharuan, Punjab

Abstract

Leukemia is a cancer of the bone marrow, a spongy tissue that secretes into the bones and serves as the site for the production of blood cells. One of the most prevalent kinds of leukemia in adults is acute myeloid leukemia (AML). Leukemia has non-specific signs and symptoms that are also similar to those of other interpersonal illnesses. The only way to accurately diagnose leukemia is by manually examining a stained blood smear or bone marrow aspirate under the microscope. However, this approach takes more time and is less precise. This paper describes a method for the automatic recognition and classification of AML in blood smears. Classification techniques include decision trees, logistic regression, support vector machines, and naive bayes.

Keywords

automatic leukemia detection, acute lymphoblastic leukemia, lymphocyte image segmentation, machine learning

Reference

  1. Cristianini, N., and J. Shawe-Taylor. “An Introduction to support vector machines and other kernel-based learning methods” New York: Cambridge University Press, 2000. 
  2. Vapnik, V. N. “The Ature of Statistical Learning Theory” New York: Springer, 1995. 
  3. A. Madabhushi, “Digital pathology image analysis: opportunities and challenges,” Imaging in Medicine, vol. 1, no. 1, pp. 7– 10, 2009. 
  4. A. N. Esgiar, R. N. G. Naguib, B. S. Sharif, M. K. Bennett, and A. Murray, “Fractal analysis in the detection of colonic cancer images,” IEEE Transactions on Information Technology in Biomedicine, vol. 6, no. 1, pp. 54–58, 2002. 
  5. L. Yang, O. Tuzel, P. Meer, and D. J. Foran, “Automatic image analysis of histopathology specimens using concave vertex graph,” in Medical Image Computing and Computer-Assisted Intervention—MICCAI 2008, pp. 833– 841, Springer, Berlin, Germany, 2008. 
  6. R. C. Gonzalez, Digital Image Processing, Pearson Education India, 2009.
  7. S. Liao, M. W. K. Law, and A. C. S. Chung, “Dominant local binary patterns for texture classification,” IEEE Transactions on Image Processing, vol. 18, no. 5, pp. 1107–1118, 2009. 
  8. J. C. Caicedo, A. Cruz, and F. A. Gonzalez, “Histopathology image classification using a bag of features and kernel functions,” in Artificial Intelligence in Medicine, vol. 5651 of Lecture Notes in Computer Science, pp. 126–135, Springer, Berlin, Germany, 2009. 
  9. H. S. Wu, J. Barba, and J. Gil, “Iterative thresholding for segmentation of cells from noisy images” Journal of Microscopy, vol.197, no. 3, pp. 296–304, 2000.

image
Nakul Magotra
Corresponding author

Chandigarh University, Gharuan, Punjab

Nakul Magotra, A Step Toward the Future: Using Machine Learning to Detect Leukemia, Int. J. in Pharm. Sci., 2023, Vol 1, Issue 7, 317-325. https://doi.org/10.5281/zenodo.8187907

More related articles
An Overview on Artificial Blood...
Rajnandini Ahire, Mukesh Ratnaparkhi, Shital Kshirsagar, Devang M...
Formulation And In vitro Evaluation Of Bilayer Tab...
Mayur Aware, Sheetal Gondkar, Rishikesh Bachhav, ...
A Comprehensive Review On Herbal Plants Showing An...
Saurabh Dumbre, Gaurav Gawade, Swati Jadhav, Pravin Khamkar, ...
Gastroretentive Drug Delivery System : A Review...
Suraj Kothule, Smita Aher, Rishikesh Bachhav, ...
Bilayer Floating Tablet : A Novel Floating Tablet...
Mayur Aware, Sheetal Gondkar, Rishikesh Bachhav, ...
Related Articles
Ayurvedic Remedies of Bacterial Meningitis...
Komal Ushir, Dinesh Rishipathak, Pragati Bhosale, Manisha Raut, ...
More related articles
An Overview on Artificial Blood...
Rajnandini Ahire, Mukesh Ratnaparkhi, Shital Kshirsagar, Devang Mahale, ...
A Comprehensive Review On Herbal Plants Showing Anti Diabetic Activity...
Saurabh Dumbre, Gaurav Gawade, Swati Jadhav, Pravin Khamkar, ...
An Overview on Artificial Blood...
Rajnandini Ahire, Mukesh Ratnaparkhi, Shital Kshirsagar, Devang Mahale, ...
A Comprehensive Review On Herbal Plants Showing Anti Diabetic Activity...
Saurabh Dumbre, Gaurav Gawade, Swati Jadhav, Pravin Khamkar, ...