Proposal of Local Automatic Weighing Attribute in CBIR.
Lung cancer is the most common malignant lesion and the principal cause of cancer-related death worldwide. This problem encourages researchers to build computer-aided solutions to help diagnose lung cancer. Content-based image retrieval (CBIR) systems are very promising in this context due to a large number of image generated everyday. However, semantic gaps have limited CBIR applicability. This work proposes a new approach to automatically adjust CBIR attribute weights to reflect users' semantic interpretation on retrieval process, minimizing the semantic gap problem and improving retrieval accuracy.
Lab of Telemedicine and Medical Informatics at University Hospital HUPPA/UFAL/EBSERH.
David
D
Jones Ferreira de Lucena
Lab of Telemedicine and Medical Informatics at University Hospital HUPPA/UFAL/EBSERH.
Marcelo
M
Costa Oliviera
Computing Institute, Federal University of Alagoas, Brazil.
Aydano
A
Pamponet Machado
D001938:Brazil; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records / Q000458:organization & administration*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D000069550:Machine Learning; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D011873:Radiology Information Systems / Q000458:organization & administration*; D012660:Semantics; D009626:Terminology as Topic; D018875:Vocabulary, Controlled