Automatic segmentation and classification of lung tumour using advance sequential minimal optimisation techniques. Issue 14 (21st October 2020)

• Record Type: Journal Article
• Authors: Vijila Rani, K.; Joseph Jawhar, S.
• Is Part Of: IET image processing. Volume 14:Issue 14(2020)
• Page Start: 3355
• Page End: 3365
• Publication Date: 2020-10-21
• Subjects: pattern classification -- medical image processing -- image segmentation -- cancer -- image classification -- Bayes methods -- feature extraction -- computerised tomography -- optimisation -- support vector machines -- image enhancement -- tumours -- lung; automatic segmentation -- advance sequential minimal optimisation techniques -- lung cells -- pulmonary tumour -- successful treatment planning -- early tumour detection -- lung tumour diagnosis -- lung tumour sensing -- characteristics classification -- image acquisition here input lung image -- improved histogram‐based fast 2D Otsu's thresholding -- lung tumour segmentation purposes -- linear discriminant analysis classifier -- support vector machine classifier -- SVM–sequence minimal optimisation classifier -- SVM–advance sequence minimal optimisation classification; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2020.0407
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : A chronic disorder caused by abnormal growth of the lung cells in the pulmonary tumour. This study suggests a modern automated approach to improve efficiency and decrease the difficulty of lung tumour diagnosis. The proposed algorithm for lung tumour sensing consists of four phases: pre‐processing, segmentation, extraction, and characteristics classification. The first stage is the image acquisition here input lung image is read and then resized. The second stage is the image pre‐processing here Perona–Malik diffusion with unsharp masking filter is proposed for enhancement purposes. The third stage is the segmentation here the improved histogram–based fast 2D Otsu's thresholding is proposed for lung tumour segmentation purposes. Finally, linear discriminant analysis classifier, support vector machine (SVM) classifier, SVM–sequence minimal optimisation classifier, Naive Bayes classifier, SVM–advance sequence minimal optimisation (SVM–ASMO) classification [proposed] included in the various classifier groups adopted in this report. Overall performance accuracy of 0.962 is obtained using the proposed SVM–ASMO method that helps to diagnose the cancer cells using the feature extraction process, which is done automatically. The specificity, precision, recall, and F 1 score of the proposed method is found to be a value of 0.984, 0.974, 0.98, and 0.984, respectively.