Comparison of Feedforward Network and Radial Basis Function to Detect Leukemia

被引：3

作者：

Bagwari, Pragya ^{[1
]}

Saxena, Bhavya

Balodhi, Meenu ^{[2
]}

Bijalwan, Vishwanath ^{[1
]}

机构：

[1] Gopeshwar Uttarakhand Govt Inst, Gopeshwar, Uttarakhand, India

[2] Uttaranchal Univ Dehradun, Dehra Dun, Uttarakhand, India

来源：

INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL INTELLIGENCE | 2017年 / 4卷 / 05期

关键词：

K-mean Clustering; Texture Features; Feed Forward and RBFNN; CELLS;

D O I：

10.9781/ijimai.2017.4510

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Leukemia is a fast growing cancer also called as blood cancer. It normally originates near bone marrow. The need for automatic leukemia detection system rises ever since the existing working methods include labor-intensive inspection of the blood marking as the initial step in the direction of diagnosis. This is very time consuming and also the correctness of the technique rest on the worker's capability. This paper describes few image segmentation and feature extraction methods used for leukemia detection. Analyzing through images is very important as from images; diseases can be detected and diagnosed at earlier stage. From there, further actions like controlling, monitoring and prevention of diseases can be done. Images are used as they are cheap and do not require expensive testing and lab equipment. The system will focus on white blood cells disease, leukemia. Changes in features will be used as a classifier input.

引用

页码：55 / 57

页数：3

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