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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.
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Full text: Available 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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Full text: Available 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