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Classifying Lung Cancer as Benign and Malignant Nodule Using ANN of Back-Propagation Algorithm and GLCM Feature Extraction on Chest X-Ray Images

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Abstract

Lung cancer is the most suffering disease which is very difficult to identify in advance and it is not easily cure if the stage of cancer becomes more malignant, the lung cancer is similar like other cancers such as breast cancer, colorectal cancer, brain tumour etc. Now-a-days, there are lot of technologies are developed to predict and treating the diseases, but still have some trouble in detecting the cancer nodule more accurately. Due to increasing in number of patients admitted in clinic, hospitals, etc., doctors cannot able to monitor every patient with high care and they failed to guide their patients with greater attention. Accordingly, the radiologists require a technology named Computer Aided Design (CAD) system for precise recognition and classification of lung nodule where the detected node is cancerous or non-cancerous. In the proposed research, the Chest X-Ray (CXR) images are used as an input image for experimenting the research and image processing techniques has been used to classify the nodule as benign or malignant and executed with greater accuracy in prediction and classification level. In this proposed research work, features were extracted from hasil segmentation image by using Grey Level Co- occurrence Matrix (GLCM) method. The extracted features from image are taken as input data and processed with Artificial Neural Network (ANN) Classifier. The classification and training has been done by Artificial Neural Network with back propagation (ANN-BP) method; therefore, the Artificial Neural Network has competitive and greater in executing the results by comparing with the existing methods of Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). Therefore, the performance evaluation of Artificial Neural Network has less training time with better accuracy of 87.5%, sensitivity of 97.75% and specificity of 89.75% by classifying the detected nodule as benign or malignant.

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Authors and Affiliations

  1. Department of Computer Science, Bharathiar University, Coimbatore, India

    D. Napoleon & I. Kalaiarasi

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  1. D. Napoleon
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  2. I. Kalaiarasi
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Contributions

The main contribution of this research work is to classifying the lung cancer as benign and malignant by using Artificial Neural Network with Back-Propagation Neural Network. The experiment of this research work has been done by using Chest X-Ray or CXR images for classifying the nodule candidate. The main advantage of using the CXR imaging technology is cost effective, non-invasive for this reason, it is suitable for all kind of people. First, the pre-processing has been done by using statistical properties (Window length, Number of Impulses) of Median filter which is very effective in removing of impulse noise from an input image. The segmentation has been done by K-Means clustering which is segmenting the hasil (mask value) image such as left and right side of the lung. The features has been extracted by using Grey Level Co-occurrence Matrix ( GLCM).The eight important features are used for further processing.

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Correspondence to D. Napoleon.

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All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. Authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript. The following authors have affiliations with organizations with direct or indirect financial interest in the subject matter discussed in the manuscript.

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Napoleon, D., Kalaiarasi, I. Classifying Lung Cancer as Benign and Malignant Nodule Using ANN of Back-Propagation Algorithm and GLCM Feature Extraction on Chest X-Ray Images. Wireless Pers Commun 126, 167–195 (2022). https://doi.org/10.1007/s11277-022-09594-1

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