How AI Can Help in the Diagnostic Dilemma of Pulmonary Nodules
| dc.contributor.author | Fahmy, Dalia | |
| dc.contributor.author | Kandil, Heba | |
| dc.contributor.author | Khelifi, Adel | |
| dc.contributor.author | Yaghi, Maha | |
| dc.contributor.author | ETAL: | |
| dc.date.accessioned | 2022-05-17T07:04:19Z | |
| dc.date.accessioned | 2023-08-19T08:18:18Z | |
| dc.date.available | 2022-05-17T07:04:19Z | |
| dc.date.available | 2023-08-19T08:18:18Z | |
| dc.date.issued | 2022-04 | |
| dc.description.abstract | Pulmonary nodules are the precursors of bronchogenic carcinoma, its early detection facilitates early treatment which save a lot of lives. Unfortunately, pulmonary nodule detection and classification are liable to subjective variations with high rate of missing small cancerous lesions which opens the way for implementation of artificial intelligence (AI) and computer aided diagnosis (CAD) systems. The field of deep learning and neural networks is expanding every day with new models designed to overcome diagnostic problems and provide more applicable and simply used models. We aim in this review to briefly discuss the current applications of AI in lung segmentation, pulmonary nodule detection and classification. | en_US |
| dc.identifier.citation | Fahmy, D., Kandil, H., Khelifi, A., Yaghi, M., Ghazal, M., Sharafeldeen, A., ... & El-Baz, A. (2022). How AI Can Help in the Diagnostic Dilemma of Pulmonary Nodules. Cancers, 14(7), 1840. | en_US |
| dc.identifier.doi | https://doi.org/10.3390/cancers14071840 | |
| dc.identifier.uri | https://edms.wexl.in/handle/1/3411 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | MDPI | en_US |
| dc.subject | Pulmonary nodule | en_US |
| dc.subject | Artificial intelligence | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Neural networks | en_US |
| dc.title | How AI Can Help in the Diagnostic Dilemma of Pulmonary Nodules | en_US |
| dc.title.alternative | journal Artical | en_US |
| dc.type | Article | en_US |