Performance Analysis of Certain Classifiers for Liver CT Images
P. Malin Bruntha1, S. Dhanasekar2, J. Grace Jency3, S. Immanuel Alex Pandian4, Prashant S Pillai5, Steven Pramod T6, Vaibhav Malani7
1P.Malin Bruntha, Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
2S. Dhanasekar, Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
3J. Grace Jency, Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
4S. Immanuel Alex Pandian, Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
5Prashant S Pillai, Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
6Steven Pramod T, Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
7Vaibhav Malani, Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 1566-1570 | Volume-8 Issue-7, May 2019 | Retrieval Number: G6009058719/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Liver is the largest internal organ and is vital for the human body’s survival. It is prone to many diseases such as Liver tumor, Fibrosis, etc. In order to know the condition of the liver, the most commonly preferred method is biopsy. But since it is a very complex and complicated procedure it is being replaced by computer-aided diagnosis (CAD) where the liver is classified into various types like normal, abnormal etc. In this study, we have compared the performance of the CAD systems namely six classifiers for CT image classification. The data of 26 patients was taken into consideration and their status was confirmed by a radiologist. The images were separated into normal and abnormal based on textural features and based on these features the performance of each classifier has been evaluated for the parameters such as accuracy, specificity and sensitivity. Amongst all the classifiers we found out that the best results were obtained for k-NN with accuracy of 88.5%.
Keyword: CAD, k-NN, SVM, Textural features
Scope of the Article: Predictive Analysis.