Lung Tumor Segmentation and Classification by Using Machine Learning Approach

Abstract :

The project proposes an improve method of Lung image classification and image segmentation approach. It is automatic support system for stage classification using learning machine and to detect Lung Tumor through Lloyds clustering method for bio medical application. Automated classification and detection of tumours in different medical images is motivated by the necessity of high accuracy when dealing with a human life. The detection of the Lung Tumor is a challenging problem, due to the structure of the Tumor cells. This project presents a segmentation method, Lloyds clustering algorithm, for segmenting Lung images to detect Tumor in its early stages and to analyze anatomical structures. The artificial neural network will be used to classify the stage of Lung Tumor that is benign, malignant and normal. Here DWT decomposition is used to analysis texture of an image. The segmentation results will be used as a base for a Computer Aided Diagnosis (CAD) system for early detection of Lung Tumor which will improves the chances of survival for the patient. Probabilistic Neural Network with radial basis function will be employed to implement an automated Lung Tumor classification. Decision making was performed in two stages: feature extraction using GLCM and the classification using PNN-RBF network. The performance of this classifier was evaluated in terms of training performance and classification accuracies. The simulated results will be shown that classifier and segmentation algorithm provides better accuracy than previous method.

Author Name : S.S.Deepthi, C.Vinola, K. Raja Sundari & K.Siva Kumar

Keywords: Computed Tomography (CT), X-ray, Thresholding, Segmentation, Discrete Wavelet Transformation (DWT), Grey Level Co – Occurrence Matrix (GLCM), Probabilistic Neural Network (PNN), Radial Basics Function (RBF), Lloyds clustering algorithm.