A new fusion model for classification of the lung diseases using genetic algorithm

Bhuvaneswari, C.; Aruna, P.; Loganathan, D.

doi:10.1016/j.eij.2014.05.001

Cited by 26 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on 150 CT images of tumorous lung and non-tumor patients obtained by the NCI Lung Cancer Database Consortium, the proposed CAD system called smart lung tumor detection has been implemented [28]. Preprocessing of the input image is considered a significant phase in research related to clinical applications of neuroimaging for improving image quality.…”

Section: Image Pre-processingmentioning

confidence: 99%

Smart Lung Tumor Prediction Using Dual Graph Convolutional Neural Network

Alameen¹

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

A significant advantage of medical image processing is that it allows non-invasive exploration of internal anatomy in great detail. It is possible to create and study 3D models of anatomical structures to improve treatment outcomes, develop more effective medical devices, or arrive at a more accurate diagnosis. This paper aims to present a fused evolutionary algorithm that takes advantage of both whale optimization and bacterial foraging optimization to optimize feature extraction. The classification process was conducted with the aid of a convolutional neural network (CNN) with dual graphs. Evaluation of the performance of the fused model is carried out with various methods. In the initial input Computer Tomography (CT) image, 150 images are pre-processed and segmented to identify cancerous and non-cancerous nodules. The geometrical, statistical, structural, and texture features are extracted from the preprocessed segmented image using various methods such as Gray-level co-occurrence matrix (GLCM), Histogram-oriented gradient features (HOG), and Gray-level dependence matrix (GLDM). To select the optimal features, a novel fusion approach known as Whale-Bacterial Foraging Optimization is proposed. For the classification of lung cancer, dual graph convolutional neural networks have been employed. A comparison of classification algorithms and optimization algorithms has been conducted. According to the evaluated results, the proposed fused algorithm is successful with an accuracy of 98.72% in predicting lung tumors, and it outperforms other conventional approaches.

show abstract

Section: Image Pre-processingmentioning

confidence: 99%

Smart Lung Tumor Prediction Using Dual Graph Convolutional Neural Network

Alameen¹

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…C. Bhuvaneswari et al, [3] proposed a method to detect normal lung, bronchitis, emphysema, pleural effusion using 400 images of lung disease. The work involved preprocessing the images, novel fusion based feature extraction, feature selection by genetic algorithm and classification.…”

Section: Literature Surveymentioning

confidence: 99%

Classification of Lung Disease using Local and Global Descriptors

Pradeebha¹,

Karpagavalli²

2016

IJCA

View full text Add to dashboard Cite

Recent trends indicate that instances of chronic respiratory diseases are on the rise in India mainly due to vehicular pollution, air and dust pollution, habit of smoking and also increased population. A World Health Organization report indicates that India has a ranking number one in the world for lung disease deaths. Respiratory diseases like asthma, chronic obstructive pulmonary disease (COPD), Interstitial Lung Disease (ILD), pneumonia, tuberculosis (TB) are emerging as most important health problems in the country. The proposed work is aimed at establishing more advanced diagnostic strategy for lung diseases using CT scan images. Lung diseases such as Emphysema, Pneumonia, Bronchitis are classified using CT scan images which is collected from National Biomedical Imaging Archive (NBIA). A total of 366 images are used, out of which 300 images are used for training and 66 images are used for testing. The classification task carried out with classifier support vector machine (SVM) using Histogram of Oriented Gradient (HOG)-global descriptors and Local Binary Pattern (LBP)local descriptors. The performance of the model built using Support Vector Machine indicates that it is effective in the prediction of lung disease with 98% predictive accuracy.

show abstract

“…Bhuvaneswari et al [16] described feature selection through genetic algorithm and the classification is done through KNN, NN, MLPNN, and J48 methods to classify the lung image dataset. This system has been tested with lung images and it achieved satisfactory results in lung diseases image classification.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Data Mining Algorithms for Image Classification

Thamilselvana¹,

Sathiaseelan²

2015

IJEME

View full text Add to dashboard Cite

Data mining is an important research area in computer science. It is a computational process of determining patterns in large data. Image mining is one of important techniques in data mining, which involved in multiple disciplines. Image Classification Refers the tagging the images into a number of predefined sets. It's also includes image preprocessing, feature extraction, object detection, object classification, object segmentation, object classification and many more techniques.

show abstract

A new fusion model for classification of the lung diseases using genetic algorithm

Cited by 26 publications

References 8 publications

Smart Lung Tumor Prediction Using Dual Graph Convolutional Neural Network

Smart Lung Tumor Prediction Using Dual Graph Convolutional Neural Network

Classification of Lung Disease using Local and Global Descriptors

A Comparative Study of Data Mining Algorithms for Image Classification

Contact Info

Product

Resources

About