BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification

A significant issue in computer-aided diagnosis (CAD) for medical applications is brain tumor classification. Radiologists could reliably detect tumors using machine learning algorithms without extensive surgery. However, a few important challenges arise, such as (i) the selection of the most import...

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Veröffentlicht in:	Scientific reports Jg. 14; H. 1; S. 5895 - 20
Hauptverfasser:	Ullah, Muhammad Sami, Khan, Muhammad Attique, Almujally, Nouf Abdullah, Alhaisoni, Majed, Akram, Tallha, Shabaz, Mohammad
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 11.03.2024 Nature Publishing Group Nature Portfolio
Schlagworte:	692/308 692/617 Accuracy Algorithms Brain - diagnostic imaging Brain cancer Brain Neoplasms - diagnostic imaging Brain tumor Brain tumors Classification Deep Learning Explinable AI Feature selection Humanities and Social Sciences Humans Learning algorithms Machine learning MRI multidisciplinary Neural networks Neural Networks, Computer Residual blocks Science Science (multidisciplinary) Stack encoders Brain tumor Explinable AI Feature selection MRI Classification Stack encoders Residual blocks Fusion
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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