Fast and efficient lung disease classification using hierarchical one-against-all support vector machine and cost-sensitive feature selection

Chang, Yongjun; Kim, Namkug; Lee, Youngjoo; Lim, Jae Jun; Seo, Joon Beom; Lee, Young Kyung

doi:10.1016/j.compbiomed.2012.10.001

Cited by 15 publications

(3 citation statements)

References 35 publications

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“…Chang et al [23] propose an efficient hierarchical classification scheme and cost-based group-feature selection criterion to improve feature calculation and classification accuracy. This approach adopts computational cost-sensitive group-feature selection criterion with the Sequential Forward Selection (SFS) to obtain the class-specific quasioptimal feature set.…”

Section: Cost-sensitive Feature Selectionmentioning

confidence: 99%

An Efficient Cost-Sensitive Feature Selection Using Chaos Genetic Algorithm for Class Imbalance Problem

Bian

Peng

Wang

et al. 2016

Mathematical Problems in Engineering

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In the era of big data, feature selection is an essential process in machine learning. Although the class imbalance problem has recently attracted a great deal of attention, little effort has been undertaken to develop feature selection techniques. In addition, most applications involving feature selection focus on classification accuracy but not cost, although costs are important. To cope with imbalance problems, we developed a cost-sensitive feature selection algorithm that adds the cost-based evaluation function of a filter feature selection using a chaos genetic algorithm, referred to as CSFSG. The evaluation function considers both feature-acquiring costs (test costs) and misclassification costs in the field of network security, thereby weakening the influence of many instances from the majority of classes in large-scale datasets. The CSFSG algorithm reduces the total cost of feature selection and trades off both factors. The behavior of the CSFSG algorithm is tested on a large-scale dataset of network security, using two kinds of classifiers: C4.5 andk-nearest neighbor (KNN). The results of the experimental research show that the approach is efficient and able to effectively improve classification accuracy and to decrease classification time. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.

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Section: Cost-sensitive Feature Selectionmentioning

confidence: 99%

An Efficient Cost-Sensitive Feature Selection Using Chaos Genetic Algorithm for Class Imbalance Problem

Bian

Peng

Wang

et al. 2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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“…Numerous pathologists have focused on Computer-aided diagnosis to investigate on lung cancer and its types. Also detecting multiple objects and classifying tumour from massive datasets is difficult [21][22][23]. Based on the survey conducted by big data research, deep learning techniques and Architectures can enhance the diagnostic accuracy rate by identifying and making decisions in the biomedical field Deep networks learn in-depth features and identify a necessary object by utilising a series of deconvolution and convolution layers.…”

Section: Introductionmentioning

confidence: 99%

Lung Disease Classification using Dense Alex Net Framework with Contrast Normalisation and Five-Fold Geometric Transformation

Rana

Sharma

Gupta

2023

IJRITCC

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lung disease is one of the leading causes of death worldwide. Most cases of lung diseases are found when the disease is in an advanced stage. Therefore, the development of systems and methods that begin to diagnose quickly and prematurely plays a vital role in today's world. Currently, in detecting differences in lung cancer, an accurate diagnosis of cancer types is needed. However, improving the accuracy and reducing training time of the diagnosis remains a challenge. In this study, we have developed an automated classification scheme for lung cancer presented in histopathological images using a dense Alex Net framework. The proposed methodology carries out several phases includes pre-processing, contrast normalization, data augmentation and classification. Initially, the pre-processing step is accompanied to diminish the noisy contents present in the image. Contrast normalization has been explored to maintain the same illumination factor among histopathological lung images next to pre-processing. Afterwards, data augmentation phase has been carried out to enhance the dataset further to avoid over-fitting problems. Finally, the Dense Alex Net is utilized for classification that comprises five convolutional layers, one multi-scale convolution layer, and three fully connected layers. In evaluation experiments, the proposed approach was trained using our original database to provide rich and meaningful features. The accuracy attained by the proposed methodology is93%, which is maximum compared with the existing algorithms

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“…Among them SVM is considered to be one of the most successful ones [2]. For example, to improve time and accuracy in differentiating diffuse interstitial lung disease for computer-aided quantification, a hierarchical SVM is introduced which shows promise for various real-time and online image-based classification applications in clinical fields [3]. SVM as a classifier is used for liver disorders and its correct classification rate is highly successful compared to the other results attained [4].…”

Section: Introductionmentioning

confidence: 99%

New Fuzzy Support Vector Machine for the Class Imbalance Problem in Medical Datasets Classification

Wang

2014

The Scientific World Journal

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In medical datasets classification, support vector machine (SVM) is considered to be one of the most successful methods. However, most of the real-world medical datasets usually contain some outliers/noise and data often have class imbalance problems. In this paper, a fuzzy support machine (FSVM) for the class imbalance problem (called FSVM-CIP) is presented, which can be seen as a modified class of FSVM by extending manifold regularization and assigning two misclassification costs for two classes. The proposed FSVM-CIP can be used to handle the class imbalance problem in the presence of outliers/noise, and enhance the locality maximum margin. Five real-world medical datasets, breast, heart, hepatitis, BUPA liver, and pima diabetes, from the UCI medical database are employed to illustrate the method presented in this paper. Experimental results on these datasets show the outperformed or comparable effectiveness of FSVM-CIP.

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Fast and efficient lung disease classification using hierarchical one-against-all support vector machine and cost-sensitive feature selection

Cited by 15 publications

References 35 publications

An Efficient Cost-Sensitive Feature Selection Using Chaos Genetic Algorithm for Class Imbalance Problem

An Efficient Cost-Sensitive Feature Selection Using Chaos Genetic Algorithm for Class Imbalance Problem

Lung Disease Classification using Dense Alex Net Framework with Contrast Normalisation and Five-Fold Geometric Transformation

New Fuzzy Support Vector Machine for the Class Imbalance Problem in Medical Datasets Classification

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