Cancer Cell Detection in Human Blood Samples using Microscopic Images : A Comprehensive Approach with CNN Classification

Authors

  • Pandugayala Karishma M.Tech Student, Department of Electronics and Communication Engineering, S.V.University College of Engineering, Tirupati, A.P. India Author
  • Dr. R. V. S. Satya Narayana Professor, Department of Electronics and Communication Engineering, S.V.University College of Engineering, Tirupati, A.P. India Author

Keywords:

Blood Cell, Abnormal Cell, Image Processing, Image Segmentation, Image Enhancement, Thresholding Techniques

Abstract

Blood testing is now considered one of the most significant clinical exams. The features of a blood cell (volume, shape, and colour) can provide important information about a patient's health. Manual inspection, on the other hand, is time-consuming and necessitates a high level of technical understanding. As a result, automatic medical diagnosis technologies are required to assist clinicians in quickly and accurately identifying disorders. The primary goal of blood cell segmentation is to isolate defective/abnormal cells from a complex background and segment it into morphological components using image processing techniques like contrast enhancement, thresholding, morphological operations etc. The suggested technique utilized here minimizes noise and improves segmentation visually. All earlier approaches used various segmentation strategies, resulting in lower efficiency than the proposed method. This work can be implemented using MATLAB environment.

Downloads

Download data is not yet available.

References

Ritika, Sandeep Kaur, “ContrastEnhancement Techniques for Images– A Visual Analysis”, International Journal of Computer Applications (0975 – 8887), Volume 64– No.17, February 201.

R., Adollah, M.Y., Mashor, N.F.M, Nasir, H., Rosline, H., Mahsin, H., Adilah, “Blood Cell Image Segmentation: A Review”, Biomed2008, Proceedings 21, 2008, pp. 141-144.

N., Ritter, J., Cooper, “Segmentation and Border Identification of Cells in Images of Peripheral Blood Smear Slides”, 30thAustralasian Computer Science Conference, Conference in Research and Practice in Information Technology, Vol. 62, 2007, pp. 161-169.

D.M.U., Sabino, L.D.F., Costa, L.D.F., E.G., Rizzatti, M.A., Zago, “A Texture Approach to Leukocyte Recognition”, Real Time Imaging, Vol. 10, 2004, pp. 205-206.

Abdul Nasir, Mustafa N, Mohd Nasir, “ Application of Thresholding in Determing Ratio of Blood Cells for Leukemia Detection” in the Proceedings of International Conference on Man-Machine system (ICoMMS) oct 2009 ,Batu Ferringhi, Penang, Malaysia.

Bhagyashri G Patil, Prof. Sanjeev N.Jain , “Cancer Cells Detection Using Digital Image Processing Methods” in International Journal of Latest Trends in Engineering and Technology”.

Downloads

Published

25-07-2024

Issue

Section

Research Articles

How to Cite

Cancer Cell Detection in Human Blood Samples using Microscopic Images : A Comprehensive Approach with CNN Classification. (2024). International Journal of Scientific Research in Science and Technology, 11(4), 157-164. https://ijsrst.com/index.php/home/article/view/277

Similar Articles

1-10 of 113

You may also start an advanced similarity search for this article.