• KOWSALYA.K
• Mrs. M. KAVITHA

Predicting malignant potential is one of the most critical components of a computer-aided diagnosis (CAD) system for Gastro Intestinal Stromal Tumors (GISTs). These tumors have been studied only on the basis of subjective Computed Tomography (CT) findings. Object detection plays a major role in many areas like medical imaging, aerial surveillance, optimal manipulation and analysis, surgical microscopes, etc. The objective of this paper is to develop a model for brain tumors detection and classification i.e., to classify whether the tumor is cancerous or non-cancerous using SVM algorithm. Earlier many have detected using GLCM feature extraction which works on Empirical Risk Minimization. We are using Support Vector Machine algorithm that works on structural risk minimization to classify the images. The SVM algorithm is applied to medical images for the tumor extraction, and a Simulink model is developed for the tumor classification function. This paper presents a prototype for SVM-based object detection, which classifies the images and evaluates whether the classified image is cancerous or non-cancerous. Our proposed classification techniques based on Support Vector Machines (SVM) and histogram based image segmentation are proposed and applied to brain image classification. Here feature extraction from Gastro tumor images will be carried out by gray scale, symmetrical and texture features. This intelligent system improves accuracy rate and reduces error rate of Gastro intestine tumor classification using SVM.

Gastro Intestinal Stromal Tumor, Benign and Malignant images, GLCM Feature Extraction, SVM classifiers

"STROMAL TUMOR PATTERN CLASSIFICATION USING SVM LEARNING WITH GLCM FEATURE EXTRACTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.996-1000, June-2019, Available :http://www.jetir.org/papers/JETIR1907S59.pdf

"STROMAL TUMOR PATTERN CLASSIFICATION USING SVM LEARNING WITH GLCM FEATURE EXTRACTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp996-1000, June-2019, Available at : http://www.jetir.org/papers/JETIR1907S59.pdf