Explainable Convolutional Neural Networks for Brain Cancer Detection and Localisation.
Sensors (Basel)
; 23(17)2023 Sep 02.
Article
en En
| MEDLINE
| ID: mdl-37688069
ABSTRACT
Brain cancer is widely recognised as one of the most aggressive types of tumors. In fact, approximately 70% of patients diagnosed with this malignant cancer do not survive. In this paper, we propose a method aimed to detect and localise brain cancer, starting from the analysis of magnetic resonance images. The proposed method exploits deep learning, in particular convolutional neural networks and class activation mapping, in order to provide explainability by highlighting the areas of the medical image related to brain cancer (from the model point of view). We evaluate the proposed method with 3000 magnetic resonances using a free available dataset. The results we obtained are encouraging. We reach an accuracy ranging from 97.83% to 99.67% in brain cancer detection by exploiting four different models VGG16, ResNet50, Alex_Net, and MobileNet, thus showing the effectiveness of the proposed method.
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Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Encéfalo
/
Neoplasias Encefálicas
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
Idioma:
En
Revista:
Sensors (Basel)
Año:
2023
Tipo del documento:
Article
País de afiliación:
Italia