Computer Assisted System for Detecting Pulmonary Embolism in Lungs 
M.Sucharitha1, PHV Sesha Talpa Sai2, M. L.R. Chaitanya Lahari3, P. Haseena Bee4
1Dr. M. Sucharitha, Department of Electronics and Communication Engineering, Malla Reddy College of Engineering and Technology, Hyderabad, India.
2Dr. P.H.V. Sesha Talpa Sai*, Department of Mechanical Engineering and Director R & D, Malla Reddy College of Engineering and Technology, Hyderabad, India.
3Ms. M. L. R. Chaitanya Lahari, Associate Professor, Department of Mechanical Engineering, Malla Reddy College of Engineering and Technology, Hyderabad, India.
4Ms. P. Haseena Bee, Associate Professor, Department of Mechanical Engineering, Malla Reddy College of Engineering and Technology, Hyderabad, India.
Manuscript received on October 04, 2021. | Revised Manuscript received on October 18, 2021. | Manuscript published on November 30, 2021. | PP: 89-94 | Volume-10 Issue-4, November 2021. | Retrieval Number: 100.1/ijrte.D65841110421 | DOI: 10.35940/ijrte.D6584.1110421
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: pulmonary embolism (PE) occurs when a blood artery in the lungs becomes suddenly blocked, generally owing to a blood clot. PE is a frequent life-threatening illness that should be diagnosed as soon as possible. A novel approach for automatically detecting PE in contrast-enhanced CT images is suggested in this research. To identify PE, computerized tomography (CT) is the main test to capture images. It is quick test, incursive with good quality images, enhanced contrast and multi-sliced images can be obtained. Candidate identification, feature calculation, and classification are all part of the system. The major aims of candidate detection are to include PE with even entire occlusions and to eliminate erroneous diagnosis of tissue and parenchymal disorders. When calculating characteristics, the location and structure of the pulmonary vascular tree, as well as the severity, form, and size of an embolus, are all taken into consideration. The ability of the CAD tool to identify emboli in the sectional and sub sectional pulmonary Arterial Tree (PAT) was examined.
Keywords: Computer Assisted Detection, Mediastinum, Pulmonary Embolism, Computerized Tomography.