Identify Cancer in Affected Bronchopulmonary Lung Segments Using Gated-SCNN Modelled with RPN
| Autor: | S. A. Aljasar, N. J. Francis, N. S. Francis, Muhammad Saqib, Yubin Xu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Lung
Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Region proposal Cancer Pattern recognition Image processing 02 engineering and technology Gateway (computer program) medicine.disease 03 medical and health sciences ComputingMethodologies_PATTERNRECOGNITION 0302 clinical medicine medicine.anatomical_structure 0202 electrical engineering electronic engineering information engineering medicine Medical imaging 020201 artificial intelligence & image processing 030212 general & internal medicine Artificial intelligence business |
| Zdroj: | 2020 IEEE 6th International Conference on Control Science and Systems Engineering (ICCSSE). |
| DOI: | 10.1109/iccsse50399.2020.9171947 |
| Popis: | In this research, a Gated-SCNN is modelled with the Region Proposal Network (RPN) to identify cancerous CT lung images. The model is based on the principle of passing CT image scans via a double gateway. By doing so, the bronchopulmonary lung segments are isolated and identified with the help of the VGG RPN model, and cancer clusters are identified with the help of an IPS-QPSO model. The output of the two gateways containing CT Scans of segments and cancer clusters are concatenated/gated to produce the final required output. |
| Databáze: | OpenAIRE |
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