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SBIR Based Screening for Lung Cancer


Affiliations
1 Department of ETCE, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India
     

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Lung cancer is the most dangerous type of cancer in the world. Early detection can save the life and increase the survivability of patients. In this project we obtain a solution for lung cancer symptom detection by applying Shape based image retrieval (SBIR). Our algorithm is broadly divided into three parts, at first part we accept the data set of cancer symptoms which is a generalized way for creating the patterns for Lung Cancer Framework, and in the second part we find the relevant data from the patterns using segmentation approach. We can choose the frequent symptoms only by using the threshold value. Based on the threshold value we decide whether it’s a cancer cell or non-cancer cell. We initialize the cancer cell value to support the pattern of cancer symptoms. It is updated in each trial. By updating the cancer cell value in each step we can check the symptom precision which either increases the accuracy or decreases it. Finally, result analysis can be proved by the appropriately using artificial neural network algorithm.

Keywords

Dicom, SVM, ANN, k-NN, Accuracy, Sensitivity, Precision and Specificity.
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  • SBIR Based Screening for Lung Cancer

Abstract Views: 258  |  PDF Views: 0

Authors

G. Mary Valantina
Department of ETCE, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India
Z. Mary Livinsa
Department of ETCE, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India

Abstract


Lung cancer is the most dangerous type of cancer in the world. Early detection can save the life and increase the survivability of patients. In this project we obtain a solution for lung cancer symptom detection by applying Shape based image retrieval (SBIR). Our algorithm is broadly divided into three parts, at first part we accept the data set of cancer symptoms which is a generalized way for creating the patterns for Lung Cancer Framework, and in the second part we find the relevant data from the patterns using segmentation approach. We can choose the frequent symptoms only by using the threshold value. Based on the threshold value we decide whether it’s a cancer cell or non-cancer cell. We initialize the cancer cell value to support the pattern of cancer symptoms. It is updated in each trial. By updating the cancer cell value in each step we can check the symptom precision which either increases the accuracy or decreases it. Finally, result analysis can be proved by the appropriately using artificial neural network algorithm.

Keywords


Dicom, SVM, ANN, k-NN, Accuracy, Sensitivity, Precision and Specificity.

References