NDLI: A Support Vector Machine Approach for Detection of Malignancy Using DNA Ploidy Analysis

Content Provider	IEEE Xplore Digital Library
Author	Nandakumar, V. Prasad, P.H. Sheeba, V.S.
Copyright Year	2014
Description	Author affiliation: Gov. Med. Coll. Thrissur, Thrissur, India (Prasad, P.H.) \|\| Gov. Eng. Coll. Thrissur, Thrissur, India (Nandakumar, V.; Sheeba, V.S.)
Abstract	Tumors are composed of an abnormal clone of cells which are capable of continuous growth. They are classified as benign and malignant based on their biological behaviour. Malignant ones are aggressively behaving and benign ones are non-aggressively behaving. Malignant tumors shows a number of variations in their nuclear morphological pattern which help to identify them. In this paper, we propose a method using Support Vector Machine learning for the classification of Benign and Malignant tissues based on the data obtained from the DNA ploidy analysis. We define the cancer detection process as a two-class classification using SVM, the two classes being Benign and Malignant cases. SVM is trained as a nonlinear classifier to automatically detect whether a given input data belongs to Benign tissue or a Malignant tissue. The images of aspiration tissue samples cannot be directly used for the extraction of feature values since they have uneven background and intensity variations between the nuclei and surrounding cytoplasm. Hence we apply some pre-processing on the input images before feature extraction using Intensity Thresholding and Active Contour Segmentation. Ten features were derived from the input images which helps the identification of Malignant and Benign tissues. Mean and Variance of the morphological features (area and perimeter) as well as the intensity based features (Total Optical Density, Hue and Saturation) are the selected inputs given to the SVM classfier. Datasets collected from a total of 50 input images (25 benign and 25 malignant) were used for training the classifier. The classifier performance was evaluated using a test dataset of 34 cases. 100% efficiency was obtained with a Gaussian Radial Basis Function (RBF) kernel with the available test data cases.
Starting Page	138
Ending Page	142
File Size	350034
Page Count	5
File Format	PDF
e-ISBN	9781479943630
DOI	10.1109/ICACC.2014.39
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-27
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Image analysis Automated Cancer Detection DNA Feature extraction Optical imaging Support Vector Machine Kernel DNA Ploidy Cancer
Content Type	Text
Resource Type	Article

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