
	Asian Journal of Applied Sciences
	Year: 2011 \| Volume: 4 \| Issue: 6 \| Page No.: 618-629 DOI: 10.3923/ajaps.2011.618.629

An Enhanced Classification and Prediction of Neoplasm using Neural Network

Fawaz Alsaade

Abstract: Neoplasm classification and prediction is an important and challenging task. The main objective of this study was to propose an enhanced approach with features of data segmentation, data clustering and analysis in terms of cost factor and cross validation. The clinical data was collected and partitioned in some fixed size segments. Each segment was further decomposed in variant sized clusters (larger and smaller) with application of cleansing data filter for specific data conversion and translation. These clusters were then passed through different combinations of network architectures. The selection of efficient architecture was based on degree of achievement for neoplasm classification (malignant and benign) and root mean square values (smaller value depicts feasible network). This study demonstrated an enhanced approach over granular diverse data clusters and elaborated the results with graphical simulation in differentials of active and cross validation cost factors against percentage of achievement. The classification and prediction was 100% for benign and 99.05% for malignant masses with least root mean square value of 0.017. The novelty of the proposed approach is that it demonstrated results at high degree of granularity and was quite suitable for analysis of any problem size. Overall, it provided an opportunity for further work to outperform as an enhanced scalable approach for neoplasm classification and prediction with neural network.

PDF Fulltext XML References Citation Report Citation

RELATED ARTICLES:

•	Neural Network-based Constant Current Control of Dynamic Wireless Power Supply System for Electric Vehicles

•	VHDL Modeling of EMG Signal Classification using Artificial Neural Network

How to cite this article:

Fawaz Alsaade , 2011. An Enhanced Classification and Prediction of Neoplasm using Neural Network. Asian Journal of Applied Sciences, 4: 618-629.

DOI: 10.3923/ajaps.2011.618.629

URL: https://scialert.net/abstract/?doi=ajaps.2011.618.629

COMMENT ON THIS PAPER

Navigation


		Online First
		Current Issue
		Previous Issues
		Editorial Board
		Submit a Manuscript
		Guide to Authors
		Subscribe to E-alerts

Indexed In


		ASCI-Database
		Asian Digital Library
		Cambridge Scientific Abstract
		Chemical Abstract Services
		Google Scholar

Science Alert