An Attention-Based Deep Convolutional Neural Network for Brain Tumor and Disorder Classification and Grading in Magnetic Resonance Imaging

Autor:	Ioannis D. Apostolopoulos, Sokratis Aznaouridis, Mpesi Tzani
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	artificial intelligence deep learning attention module feature fusion magnetic resonance imaging Information technology T58.5-58.64
Zdroj:	Information, Vol 14, Iss 3, p 174 (2023)
Druh dokumentu:	article
ISSN:	2078-2489
DOI:	10.3390/info14030174
Popis:	This study proposes the integration of attention modules, feature-fusion blocks, and baseline convolutional neural networks for developing a robust multi-path network that leverages its multiple feature-extraction blocks for non-hierarchical mining of important medical image-related features. The network is evaluated using 10-fold cross-validation on large-scale magnetic resonance imaging datasets involving brain tumor classification, brain disorder classification, and dementia grading tasks. The Attention Feature Fusion VGG19 (AFF-VGG19) network demonstrates superiority against state-of-the-art networks and attains an accuracy of 0.9353 in distinguishing between three brain tumor classes, an accuracy of 0.9565 in distinguishing between Alzheimer’s and Parkinson’s diseases, and an accuracy of 0.9497 in grading cases of dementia.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/62ebc4d7ea9c4c60978a7a854e59b33b Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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