Online ISSN: 2515-8260

Keywords : Pulmonary Nodules


CT LUNG IMAGES CLASSIFICATION FOR NODULE DETECTION

R. Charanya; J. Josphin Mary

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1390-1393
DOI: 10.31838/ejmcm.07.09.143

Lung cancer is an early lung cancer. The world's greatest cause of death is lung cancer. Lung cancer typically does not cause signs or symptoms in its early stages. In order to reduce the mortality rate early detection of lung cancer is required. Computed Tomography (CT) lung images classification for nodule detection is discussed in this study. At first energy features are used for feature extraction. Then maximum likelihood classifier is used for classification. The experimental results show the performance of pulmonary nodules classification in terms of accuracy.

PARALLEL ALGORITHMS FOR PULMONARY NODULE CLASSIFICATION WITH THE HELP OF BAYES THEOREM

G. Sridevi; V. Shanthi; J. Josphin Mary; R. Charanya

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1394-1397
DOI: 10.31838/ejmcm.07.09.144

The cancer starts in the lungs is known as lung cancer. The leading world cause of death is lung cancer. In its early stages, lung cancer does not usually cause signs and symptoms. The early diagnosis of lung cancer is required to reduce the mortality rate. A parallel algorithm for pulmonary nodule classification with the help of Bayes theorem is discussed in this study. At first parallel algorithms like task, pipeline and data parallel for feature extraction. Then Bayes theorem is used for classification. The experimental results show the performance of pulmonary nodules classification in terms of accuracy using parallel algorithms and Bayes classification.