Lung Pattern Classification by Using Artificial Neural Network

Lung disease are the disorders that affect the lungs, the organ allow us to breathe and it is the most common medical conditions worldwide especially in India. The disease such as pleural effusion and normal lung are detected and classified in this work. It presents a computer aided classification in computer tomography images of lungs developed using artificial neural network-back propagation network. The purpose of the work is to detect and classify the lung disease by effective feature extraction through dual tree complex wavelet transform and gray level co-occurrence matrix features. We propose and evaluate the artificial neural network-back propagation network designed for classification of interstitial lung disease pattern. The parameter gives the maximum classification accuracy. Then we propose fuzzy clustering to segment the lesion part from abnormal lung

Authors

Publication Details

Journal
International Journal of Advanced Research in Science Communication and Technology
Published
2025-02-13
DOI
https://doi.org/10.48175/ijarsct-23268
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Lung Pattern Classification by Using Artificial Neural Network

B. Buvaneswari
International Journal of Advanced Research in Science Communication and Technology
Air Quality Monitoring and Forecasting
article

Lung Pattern Classification by Using Artificial Neural Network

B. Buvaneswari
article en

Abstract

Lung disease are the disorders that affect the lungs, the organ allow us to breathe and it is the most common medical conditions worldwide especially in India. The disease such as pleural effusion and normal lung are detected and classified in this work. It presents a computer aided classification in computer tomography images of lungs developed using artificial neural network-back propagation network. The purpose of the work is to detect and classify the lung disease by effective feature extraction through dual tree complex wavelet transform and gray level co-occurrence matrix features. We propose and evaluate the artificial neural network-back propagation network designed for classification of interstitial lung disease pattern. The parameter gives the maximum classification accuracy. Then we propose fuzzy clustering to segment the lesion part from abnormal lung

International Journal of Advanced Research in Science Communication and Technology
Openalex Percentile: Top 98%
Air Quality Monitoring and Forecasting
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.