Support Vector Machine Algorithm for Effective Lung Cancer Detection Using Fuzzy Clusters

Support Vector Machine Algorithm for Effective Lung Cancer Detection Using Fuzzy Clusters

Jalal deen K¹, Rachel L², Kins burk sunil N³

 ¹Asst.Prof (S.G), Department of ECE, Solamalai College of Engineering, Email id: jalaldeen.scemdu@gmail.com

  ² Assistant Professor, Department of ECE, Latha Mathavan College of Engineering, Madurai, Tamilnadu.

   ³ Associate Professor, Department of BME, Sethu Institute of technology, Virudhunagar, Tamilnadu.

Abstract—     Tuberculosis is considered the main health threat in several parts of the world. This ithe primary infectiousdisease causing this disease.When untreatedandundiagnosed, there ishigh mortalityrate in patients.Thismethodincludes an automatic method to recognize tubersThis approach requires automated tuberculosis identification techniques to reduce problematic illnesses. Extraction of the lung region is first performed using the Graph Cut segmentation system. We develop a collection of properties including textures, shapes, etc. in this area of ​​the lung. You must use an SVM to identify X-rays as normal or abnormal. The systematic system proposed for analyzing TB segmentation provides better performance than the graph cut segmentation method.

Index Terms— ANN, Segmentation, fuzzy, graph cut, SVM.