文章基本信息

标题：BFO AIS: A Framework for Medical Image Classification Using Soft Computing Techniques
本地全文：下载
作者：D. Chitra ; M. Karthikeyan
期刊名称：International Journal on Soft Computing
电子版ISSN：2229-7103
出版年度：2017
卷号：8
期号：1
页码：13
DOI：10.5121/ijsc.2017.8102
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Medical images provide diagnostic evidence/information about anatomical pathology. The growth indatabase is enormous as medical digital image equipment’s like Magnetic Resonance Images (MRI),Computed Tomography (CT), and Positron Emission Tomography CT (PET-CT) are part of clinical work.CT images distinguish various tissues according to gray levels to help medical diagnosis. Ct is morereliable for early tumours and haemorrhages detection as it provides anatomical information to plan radiotherapy. Medical information systems goals are to deliver information to right persons at the right time andplace to improve care process quality and efficiency. This paper proposes an Artificial Immune System(AIS) classifier and proposed feature selection based on hybrid Bacterial Foraging Optimization (BFO)with Local Search (LS) for medical image classification.
关键词：Computed Tomography (CT); Feature Selection; Artificial immune classifier; Correlation based Feature;Selection (CFS); Bacterial Foraging Optimization (BFO); Local Search (LS)