Abstract
Cancers are one of the deadliest diseases with a costly treatment system in the world at present. In this paper a cost-effective, autonomous system of cancer-cell detection was proposed using several efficient image processing methods to develop an early stage non-Hodgkin type lymphoma which is a type of blood cancer. The system is implemented automatically to detect the traits of cancer in microscopy images of biopsy samples. Recent attempts have previously lacked flexibility in characteristics and the accuracy level is not consistent with the individual cancer type. The framework consisted three stages for detecting cancer on the basis of various detected traits including cell segmentation, quantification, area measurement analysis of cells, a center clump detection using the moment of image, identification of 4-connected components and Moore-Neighbor tracing algorithm. This methodology has been used in several sets of images and Feedback from these test executions has been used to improve the system. Subsequently, the proposed method can be used efficiently for used for autonomous non-hodgking type lymphoma cancer cell detection, which has an accuracy of 93.75%.
Highlights
The term cancer is referred to as a barrier to anomalous cell division
Even though the process is lengthy and difficult, but knowing more will enable doctors to cure cancer patients more effectively. This motivated us to think about the mechanism of cancer detection and use technology to speed up the process
An automatic detection method was introduced by Agaian, S. et al for Acute Myelogenous Leukemia where 80 microscopic image data, collected from the American Haematology Society, were used
Summary
The term cancer is referred to as a barrier to anomalous cell division. Cancer cells can migrate across blood, lymph systems and tumors to other areas of the body. Even though the process is lengthy and difficult, but knowing more will enable doctors to cure cancer patients more effectively. This motivated us to think about the mechanism of cancer detection and use technology to speed up the process. Leukemia detection with leucocytes classification was performed by Putzu, L. et al [6] using image processing techniques including color conversation, contrast stretching, applying Zack Algorithm for segmentation, removing backgrounds and so on. Total seven types of feature extraction calculation were applied including measurement of roundness, convexity, compactness, elongations, eccentricity, rectangularity, and these were fed SVM classifier where the accuracy was more than 80% deploying on 33 test images
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Computer Science and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
