A DEEP LEARNING APPROACH LUNG SEGMENTATION AND PNEUMONIA DETECTION FROM X-RAYS
1st International Conference on Recent Trends in Microelectronics, Automation, Computing and Communications Systems, ICMACC 2022
; : 167-173, 2022.
Article
in English
| Scopus | ID: covidwho-2325759
ABSTRACT
Lung segmentation is a process of detection and identification of lung cancer and pneumonia with the help of image processing techniques. Deep learning algorithms can be incorporated to build the computer-aided diagnosis (CAD) system for detecting or recognizing broad objects like acute respiratory distress syndrome (ARDS), Tuberculosis, Pneumonia, Lung cancer, Covid, and several other respiratory diseases. This paper presents pneumonia detection from lung segmentation using deep learning methods on chest radiography. Chest X-ray is the most useful technique among other existing techniques, due to its lesser cost. The main drawback of a chest x-ray is that it cannot detect all problems in the chest. Thus, implementing convolutional neural networks (CNN) to perform lung segmentation and to obtain correct results. The 'lost' regions of the lungs are reconstructed by an automatic segmentation method from raw images of chest X-ray. © 2022 IEEE.
CAD system; CNN; Lung segmentation; U-NET; Biological organs; Computer aided diagnosis; Computer aided instruction; Convolutional neural networks; Deep learning; Image segmentation; Learning systems; Object detection; Pulmonary diseases; X ray radiography; Acute respiratory distress syndrome; Computer aided diagnosis systems; Convolutional neural network; Detection and identifications; Image processing technique; Learning approach; Learning methods; Lung Cancer; Learning algorithms
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
1st International Conference on Recent Trends in Microelectronics, Automation, Computing and Communications Systems, ICMACC 2022
Year:
2022
Document Type:
Article
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