Deep Learning Models to Predict Brain Cancer Grade Through MRI Analysis

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Vale, P (Autor) Outra; Boer, J (Autor) Outra; Oliveira, HP (Autor) FCUP; Pereira, T (Autor) Outra

Ata de Conferência Internacional

Título: 2024 IEEE 37TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, CBMS 2024

Páginas: 153-157

37th International Symposium on Computer-Based Medical Systems (CBMS)

Guadalajara, MEXICO, JUN 26-28, 2024

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-016-DYV

DOI: 10.1109/cbms61543.2024.00033

Abstract (EN): The early and accurate detection and the grading characterization of brain cancer will generate a positive impact on the treatment plan of those patients. AI-based models can help analyze the Magnetic Resonance Imaging (MRI) to make an initial assessment of the tumor grading. The objective of this work was to develop an Al-based model to classify the grading of the tumor using the MRI. Two regions of interest were explored, with several levels of complexity for the neural network architecture, and Iwo strategies to deal with Unbalanced data. The best results were obtained for the most complex architecture (Resnet50) with a combination of weighted random sampler and data augmentation achieving a balanced accuracy of 62.26%. This work confirmed that complex problems required a more dense neural network and strategies to deal with the unbalanced data.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 5