Oladimej, Oladosu Oyebisi ORCID: https://orcid.org/0000-0001-8835-6156 and Ibitoye, Ayodeji ORCID: https://orcid.org/0000-0002-5631-8507 (2023) Brain tumor classification using ResNet50-convolutional block attention module. Applied Computing and Informatics. ISSN 2634-1964 (Print), 2210-8327 (Online) (doi:10.1108/ACI-09-2023-0022)

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Abstract

Purpose – Diagnosing brain tumors is a process that demands a significant amount of time and is heavily dependent on the proficiency and accumulated knowledge of radiologists. Over the traditional methods, deep learning approaches have gained popularity in automating the diagnosis of brain tumors, offering the potential for more accurate and efficient results. Notably, attention-based models have emerged as an advanced, dynamically refining and amplifying model feature to further elevate diagnostic capabilities. However, the specific impact of using channel, spatial or combined attention methods of the convolutional block attention module (CBAM) for brain tumor classification has not been fully investigated.
Design/methodology/approach – To selectively emphasize relevant features while suppressing noise, ResNet50 coupled with the CBAM (ResNet50-CBAM) was used for the classification of brain tumors in this research.
Findings – The ResNet50-CBAM outperformed existing deep learning classification methods like convolutional neural network (CNN), ResNet-CBAM achieved a superior performance of 99.43%, 99.01%, 98.7% and 99.25% in accuracy, recall, precision and AUC, respectively, when compared to the existing classification methods using the same dataset.
Practical implications – Since ResNet-CBAM fusion can capture the spatial context while enhancing feature representation, it can be integrated into the brain classification software platforms for physicians toward enhanced clinical decision-making and improved brain tumor classification.

Item Type:	Article
Uncontrolled Keywords:	decision support; deep learning; MRI; ResNet; brain tumour; convolutional block attention mechanism
Subjects:	Q Science > Q Science (General) R Medicine > R Medicine (General) R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	05 Jul 2024 10:06
URI:	http://gala.gre.ac.uk/id/eprint/47569

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