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Content Provider | IET Digital Library |
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Author | Rani, K. Vijila Jawhar, S. Joseph |
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 F1 score of the proposed method is found to be a value of 0.984, 0.974, 0.98, and 0.984,respectively. |
Starting Page | 3355 |
Ending Page | 3365 |
Page Count | 11 |
ISSN | 17519659 |
Volume Number | 14 |
e-ISSN | 17519667 |
Issue Number | Issue 14, Dec (2020) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-ipr/14/14 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.0407 |
Journal | IET Image Processing |
Publisher Date | 2020-08-24 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Advance Sequential Minimal Optimisation Technique Automatic Segmentation Bayes Method Biology And Medical Computing Cancer Characteristic Classification Computer Vision And Image Processing Technique Computerised Tomography Early Tumour Detection Feature Extraction Image Acquisition Here Input Lung Image Image Classification Image Enhancement Image Segmentation Improved Histogram-based Fast 2D Otsu's Thresholding Knowledge Engineering Technique Linear Discriminant Analysis Classifier Lung Lung Cell Lung Tumour Diagnosis Lung Tumour Segmentation Purposes Lung Tumour Sensing Medical Image Processing Optical, Image And Video Signal Processing Optimisation Patient Diagnostic Method And Instrumentation Pattern Classification Pulmonary Tumour Statistics Successful Treatment Planning Support Vector Machine Support Vector Machine Classifier SVM–advance Sequence Minimal Optimisation Classification SVM–sequence Minimal Optimisation Classifier Tumours |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software |
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