Open AccessLeukaemia Detection in Microscopic Imagery using Optimization Algorithm
Author(s) -
M. Venkata Dasu,
P. Venkata Subbaiah
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.24.12068
Subject(s) - cuckoo search , artificial intelligence , computer science , pattern recognition (psychology) , segmentation , matlab , computer vision , sensitivity (control systems) , image segmentation , feature (linguistics) , algorithm , philosophy , particle swarm optimization , electronic engineering , engineering , operating system , linguistics
In this paper, automated approach of blood cancer detection is proposed. Usually microscopic images examined by experts manually are time consuming and less accuracy. The automated blood cancer detection system analyses the microscopic image and overcomes these drawbacks. The proposed system extracts the features of the image and applies filtering techniques. In this paper proposed method is cuckoo search optimization algorithm which is used in line with segmentation. The features of segmented image can be obtained from Scale invariant feature transform. Some of the features like PSNR, sensitivity, accuracy, etc. are calculated for leukemia detection. The performance is compared with the existing method ACO. The proposed system is tested on image dataset and 94.24% accuracy is achieved. The proposed system is successfully implemented in MATLAB.