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How AI Can Help in the Diagnostic Dilemma of Pulmonary Nodules.
Fahmy, Dalia; Kandil, Heba; Khelifi, Adel; Yaghi, Maha; Ghazal, Mohammed; Sharafeldeen, Ahmed; Mahmoud, Ali; El-Baz, Ayman.
Afiliação
  • Fahmy D; Diagnostic Radiology Department, Mansoura University Hospital, Mansoura 35516, Egypt.
  • Kandil H; Bioengineering Department, University of Louisville, Louisville, KY 40292, USA.
  • Khelifi A; Information Technology Department, Faculty of Computers and Informatics, Mansoura University, Mansoura 35516, Egypt.
  • Yaghi M; Computer Science and Information Technology Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates.
  • Ghazal M; Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates.
  • Sharafeldeen A; Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates.
  • Mahmoud A; Bioengineering Department, University of Louisville, Louisville, KY 40292, USA.
  • El-Baz A; Bioengineering Department, University of Louisville, Louisville, KY 40292, USA.
Cancers (Basel) ; 14(7)2022 Apr 06.
Article em En | MEDLINE | ID: mdl-35406614
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Screening_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Screening_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article