Classification of Liver Tumors from Computed Tomography Using NRSVM
| dc.contributor.author | Priyadarsini S. | |
| dc.contributor.author | Tavera Romero, Carlos Andrés | |
| dc.contributor.author | Mrunalini M. | |
| dc.contributor.author | Koteswara Rao, Ganga Rama | |
| dc.contributor.author | Sengan, Sudhakar | |
| dc.date.accessioned | 2025-07-03T21:36:10Z | |
| dc.date.available | 2025-07-03T21:36:10Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | A classification system is used for Benign Tumors (BT) and Malignant Tumors (MT) in the abdominal liver. Computed Tomography (CT) images based on enhanced RGS is proposed. Diagnosis of liver diseases based on observation using liver CT images is essential for surgery and treatment planning. Identifying the progression of cancerous regions and Classification into Benign Tumors and Malignant Tumors are essential for treating liver diseases. The manual process is time-consuming and leads to intra and inter-observer variability. Hence, an automatic method based on enhanced region growing is proposed for the Classification of Liver Tumors (LT). To enhance the Liver Region (LR) from the surrounding tissues, Non-Linear Mapping (NLP) is used. Region Growing Segmentation (RGS) is employed to segment the LR, and Expectation-Maximization (EM) algorithm is used to segment the region of interest. Grey Level Co-occurrence Matrix (GLCM) features are extracted from the tumor region, and Nonlinear Random Support Vector Machine (NRSVM) classification is performed to classify the Benign Tumors and Malignant Tumors. The proposed method is tested on a database of medical images collected from Med all Diagnostic Research Centre and attained an accuracy of 96%. The proposed method is beneficial for better liver tumor diagnosis in an optimized method by the medical expert. | |
| dc.identifier.citation | Priyadarsini, S., Romero, C. A. T., Mrunalini, M., Rao, G. R. K., & Sengan, S. (2022). Classification of Liver Tumors from Computed Tomography Using NRSVM. Intelligent Automation and Soft Computing, 33(3). https://doi.org/10.32604/iasc.2022.024786 | |
| dc.identifier.issn | 10798587 | |
| dc.identifier.uri | https://repositorio.usc.edu.co/handle/20.500.12421/7155 | |
| dc.language.iso | en | |
| dc.publisher | Tech Science Press | |
| dc.subject | computed tomography | |
| dc.subject | expectation maximization | |
| dc.subject | grey level co-occurrence matrix | |
| dc.subject | Liver tumor | |
| dc.subject | non-linear random support vector machine | |
| dc.subject | region growing | |
| dc.title | Classification of Liver Tumors from Computed Tomography Using NRSVM | |
| dc.type | Article |
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