Multilayered perceptron (MLP) network trained by recursive least squares algorithm

Author

Ramli, Dzati Athiar ; Saleh, Junita Mohamad ; Hashim, Fakroul Ridzuan ; Isa, Nor Ashidi Mat

Author_Institution

School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang, Malaysia

fYear

2005

Firstpage

288

Lastpage

291

Abstract

In my research, the performance of multilayered perceptron (MLP) network which trained by recursive least square (RLS) algorithm is investigated. The network has been implemented to classify the cervical cells into normal, low-grade squamos intraepithelial lesion(LSIL) and high-grade squamos intraepithelial lesion(HSIL). Based on Bathesda System, it has achieved to classify the cervical cells with high accuracy, sensitivity and specificity as well as lower false negative and false positive but more work should be done to enhance the system accuracy.

Keywords

Biomedical imaging; Cervical cancer; Instruction sets; Least squares methods; Lesions; Multilayer perceptrons; Neural networks; Resonance light scattering; Sensitivity and specificity; Testing; MLP network; Pap test; cervical cancer diagnosis system; recursive least square algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Computers, Communications, & Signal Processing with Special Track on Biomedical Engineering, 2005. CCSP 2005. 1st International Conference on

Conference_Location

Kuala Lumpur, Malaysia

Print_ISBN

978-1-4244-0011-9

Electronic_ISBN

978-1-4244-0012-6

Type

conf

DOI

10.1109/CCSP.2005.4977208

Filename

4977208