@article{(Open Science Index):https://publications.waset.org/pdf/10003283,
	  title     = {Liver Tumor Detection by Classification through FD Enhancement of CT Image},
	  author    = {N. Ghatwary and  A. Ahmed and  H. Jalab},
	  country	= {},
	  institution	= {},
	  abstract     = {In this paper, an approach for the liver tumor detection
in computed tomography (CT) images is represented. The detection
process is based on classifying the features of target liver cell to
either tumor or non-tumor. Fractional differential (FD) is applied for
enhancement of Liver CT images, with the aim of enhancing texture
and edge features. Later on, a fusion method is applied to merge
between the various enhanced images and produce a variety of
feature improvement, which will increase the accuracy of
classification. Each image is divided into NxN non-overlapping
blocks, to extract the desired features. Support vector machines
(SVM) classifier is trained later on a supplied dataset different from
the tested one. Finally, the block cells are identified whether they are
classified as tumor or not. Our approach is validated on a group of
patients’ CT liver tumor datasets. The experiment results
demonstrated the efficiency of detection in the proposed technique.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {9},
	  number    = {11},
	  year      = {2015},
	  pages     = {2355 - 2358},
	  ee        = {https://publications.waset.org/pdf/10003283},
	  url   	= {https://publications.waset.org/vol/107},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 107, 2015},
	}