Entropy Virus Microscopy Images Recognition via Neural Network Classifiers

被引：0

作者：

Shakri, Afiq Ahmad ^{[1
]}

Saidi, Syahrul Affandi ^{[1
]}

Jaafar, Haryati ^{[1
]}

Mansor, Muhammad Naufal ^{[1
]}

Mustafa, Wan Azani ^{[1
]}

Junoh, Ahmad Kadri ^{[2
]}

机构：

[1] Univ Malaysia Perlis, Dept Elect, Fac Engn Technol, Kampus Uniciti Alam, Padang Besar 02100, Perlis, Malaysia

[2] Univ Malaysia Perlis, Inst Engn Math IMK, Arau 02600, Perlis, Malaysia

来源：

2017 7TH IEEE INTERNATIONAL CONFERENCE ON CONTROL SYSTEM, COMPUTING AND ENGINEERING (ICCSCE) | 2017年

关键词：

Classifiers; Entropy; virus image;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

One of the topics that are commonly in focus of object detection and image recognition is virus detection. It is well known that to learn and detecting virus proven to be a challenging and quite complex task for computer systems under different noise level. This research work investigates the performances of preprocessing stages with Entropy feature extraction with Feed Forward Neural Network (FFNN) classifier under various levels of noise. The real time experiment conducted proved that the method proposed are efficient, robust, and excellent of which it has produced a results accuracy of up to 88% for biological viruses images classification.

引用

页码：348 / 351

页数：4

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