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An Efficient Lung Image Classification Using GDA Based Feature Reduction and Tree Classifier

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Handbook of Multimedia Information Security: Techniques and Applications

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Abstract

In recent days, one of the malignant diseases among different tumors is lung cancer. The accessible diagnosing methods and the current effects of cancer treatment are unsatisfactory. For that reason, we introduce innovative diagnostic techniques which classify cancer affected portion from the lung image at an early stage. In the study, an excellent image classification system is proposed to detect and classify the lung images as normal and abnormal. In the initial phase of our work, the lung images are fed to the preprocessing module using Histogram Equalization to remove noise and gain the clarity of the image. In addition to this, feature extraction techniques are applied and then it is reduced to the best subset of features using Generalized Discriminant Analysis (GDA). Here, the lung image classification is done by four different classifiers such as K-Nearest Neighbor (KNN), Naïve Bayes (NB), Neural Network (NN) and Random Forest (RF). The performance measures of these classifiers are analyzed and compared with one another. The results demonstrated that the RF-GDA technique accomplishes maximum classification accuracy compared to existing classification approaches.

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Authors and Affiliations

  1. Department of Computer Science, Pioneer College of Arts and Science, Coimbatore, India

    K. Vasanthi & N. Bala Kumar

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  1. K. Vasanthi
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  2. N. Bala Kumar
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Editors and Affiliations

  1. Department of Computer Science and Engineering, NIT Patna, Patna, India

    Amit Kumar Singh

  2. Department of Electronics Engineering, Indian Institute of Technology, BHU, Varanasi, Uttar Pradesh, India

    Anand Mohan

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Vasanthi, K., Kumar, N.B. (2019). An Efficient Lung Image Classification Using GDA Based Feature Reduction and Tree Classifier. In: Singh, A., Mohan, A. (eds) Handbook of Multimedia Information Security: Techniques and Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-15887-3_31

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  • DOI: https://doi.org/10.1007/978-3-030-15887-3_31

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-15887-3

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