A Computer Aided Diagnosis System for Lung Cancer Detection \Using Support Vector Machine

Gomathi, M.; Thangaraj, P.

doi:10.3844/ajassp.2010.1532.1538

Cited by 36 publications

(25 citation statements)

References 18 publications

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“…Several filter operations which intensify or reduce certain image details enable an easier or faster evaluation. The aim of pre-processing is an improvement of the image data that suppresses unwanted distortions or enhances some image features important for further processing 5 . The noise present in the input CT image is reduced by employing a median filter given in Equation,…”

Section: Image Denoisingmentioning

confidence: 99%

Performance Analysis of Firefly Search Fuzzy C-Means (Fsfcm) for Detecting Lung Cancer Nodules

Amudha¹,

Balu²,

Sakthivel³

2017

Int. Res. J. Pharm.

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Computer-Aided Diagnosis system is used for diagnosing the lung cancer nodule from the chest Computer Tomography (CT) images. The System can automatically detect and diagnosis the lung cancer nodules with efficient accuracy and it also minimize the time taken by the radiologist for interpretation. The computer-aided diagnosis system is developed by combined techniques such as image denoising, segmentation, feature extraction and classification. CT lung image is denoised by the median filter. The system automatically segments region of interest from denoised lung computer tomography image using the proposed methodologies namely FSFCM. Then, the optimal initial cluster centres are created for the FSFCM to improve the segmentation results. This work presents a modified firefly search fuzzy C-means algorithm to produce efficient segmentation results.

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Section: Image Denoisingmentioning

confidence: 99%

Performance Analysis of Firefly Search Fuzzy C-Means (Fsfcm) for Detecting Lung Cancer Nodules

Amudha¹,

Balu²,

Sakthivel³

2017

Int. Res. J. Pharm.

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“…The final learning linear algorithms proposed in recent years is the Support Vector Machine (Gomathi and Thangaraj, 2010;Bharathi and Natarajan, 2011). The main advantage of the Support Vector Machine (SVM) is that its training is performed through the solution of a linearly constrained convex quadratic programming problem.…”

Section: Introductionmentioning

confidence: 99%

A Reduction Algorithm for Analysis of Linear Neuron

Iman¹

2012

Journal of Computer Science

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Problem statement:The aim of feature selection is to select a feature set that is relevant for a given application. Feature selection is complex and remains an important issue in many domains. In the field of neural networks, feature selection has been used in many applications and their methods have been employed. In this study we present neural network approaches to feature selection. Approach: In this study a reduction algorithm of the features vector dimension was described by eliminating its selected components on the basis of analyzing the results of teaching a neuron, which has a linear activation function of the type. In the presented algorithm, the value of the mean square error, which appears after the reduction, is the criterion, on the basis of which the components of the eliminated vector were selected. The algorithm is based on the analysis of the classifier of balances vector. Results: The results of calculations obtained when analyzing the data describing an example-task of medical diagnosis were presented as an illustration. Results from the experiment indicate that the elimination of components of features vector using the Reduction algorithm did not cause the increase of the value of mean square. Conclusion: Our study showed that, results provide experimental evidences on the effectiveness of the proposed approach for feature selection in the bioinformatics applications.

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“…In [5] many techniques were applied for lung region detection. Bit plane slicing algorithm is used to generate different binary slices which then were enhanced by erosion algorithm and dilation and median filters.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Approach for Detection and Classification of Computed Tomography Lung Cancer

Alakwaa¹,

Nassef²,

Badr³

2017

ijacsa

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Abstract-The paper presents approaches for nodule detection and extraction in axial lung computed tomography. The goal is to detect correctly pulmonary nodule to recognize and screen lung cancer patients. The pulmonary nodule detection is very challenging problem. The proposed model developed a hybrid efficient model based on affine-invariant representation and shape of segmented nodule. Due to large number of extracted features for all slices on patient, feature selection is an important step to select the most important feature for classification. We apply forward stepwise least squares regression that maximizes the Rsquared value, this criterion provides a fast preprocessing feature selection assessment for systems with huge volumes of features based on a linear models framework. Moreover, gradient boosting have been suggested to select the relevant features based on boosting approach. Classification of patients has been done by support vector machine. Kaggle DSB dataset is used to test the accuracy of our model. The results show major improvement in accuracy and the features are reduced.

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A Computer Aided Diagnosis System for Lung Cancer Detection \Using Support Vector Machine

Cited by 36 publications

References 18 publications

Performance Analysis of Firefly Search Fuzzy C-Means (Fsfcm) for Detecting Lung Cancer Nodules

Performance Analysis of Firefly Search Fuzzy C-Means (Fsfcm) for Detecting Lung Cancer Nodules

A Reduction Algorithm for Analysis of Linear Neuron

An Enhanced Approach for Detection and Classification of Computed Tomography Lung Cancer

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