8th International Conference on Artificial Intelligence and Applications (AIAPP 2021)

March 27 ~ 28, 2021, Sydney, Australia

https://cosit2021.org/aiapp/index

#artificialintelligence #softcomputing

Submission Deadline: February 27, 2021

Contact Us

Here's where you can reach us : aiapp_secretary@yahoo.com or aiapp@cosit2021.org

Paper Submission URL: https://cosit2021.org/submission/index.php 

3rd International Conference on Machine Learning & Applications (CMLA 2021)

September 25 ~ 26, 2021, Toronto, Canada

https://cseit2021.org/cmla/index

Submission Deadline: February 27, 2021

Contact us:

Here's where you can reach us: cmla@cseit2021.org (or) cmlaconference@yahoo.com

Submission Link:

https://cseit2021.org/submission/index.php

Medical Image Processing Methodology for Liver Tumour Diagnosis

Thayalini Prakash

Department of Software Engineering, University of Westminster, UK

ABSTRACT

Apply the Image processing techniques to analyse the medical images may assist medical professionals as well as patients, especially in this research apply the algorithms to diagnose the liver tumours from the abdominal CT image. This research proposes a software solution to illustrate the automated liver segmentation and tumour detection using artificial intelligent techniques. Evaluate the results of the liver segmentation and tumour detection, in-cooperation with the radiologists by using the prototype of the proposed system. This research overcomes the challenges in medical image processing. The 100 samples collected from ten patients and received 90% accuracy rate.

KEYWORDS

CT abdominal image, Medical Image processing, liver segmentation, Tumour region extraction

ORIGINAL SOURCE URL: https://aircconline.com/ijsc/V8N4/8417ijsc02.pdf

http://airccse.org/journal/ijsc/current2017.html

An Efficient PSO Based Ensemble Classification Model on High Dimensional Datasets

G. Lalitha Kumari1 and N. Naga Malleswara Rao2

1Research Scholar, Acharya Nagarjuna University, Guntur, AP, India

2Professor, Dept. of IT, RVR & JC College of Engineering, Guntur, AP, India

Abstract

As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high  dimensionality and sparsity problems. Also, due to the availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection based ensemble learning models is to classify the high dimensional data with high computational efficiency and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.

Keywords

PSO, Neural network, Ensemble classification, High dimension dataset

Original Source URL: https://aircconline.com/ijsc/V8N4/8417ijsc01.pdf

http://airccse.org/journal/ijsc/current2017.html