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However, the advent of deep learning methodologies has revolutionized the field, offering more accurate and efficient assessments. Attention-based models have emerged as promising tools, focusing on salient features within complex medical imaging data. However, the precise impact of different attention mechanisms, such as channel-wise, spatial, or combined attention within the Channel-wise Attention Mode (CWAM), for brain tumor classification remains relatively unexplored. This study aims to address this gap by leveraging the power of ResNet101 coupled with CWAM (ResNet101-CWAM) for brain tumor classification. The results show that ResNet101-CWAM surpassed conventional deep learning classification methods like ConvNet, achieving exceptional performance metrics of 99.83% accuracy, 99.21% recall, 99.01% precision, 99.27% F1-score and 99.16% AUC on the same dataset. This enhanced capability holds significant implications for clinical decision-making, as accurate and efficient brain tumor classification is crucial for guiding treatment strategies and improving patient outcomes. Integrating ResNet101-CWAM into existing brain classification software platforms is a crucial step towards enhancing diagnostic accuracy and streamlining clinical workflows for physicians.<\/jats:p>","DOI":"10.1186\/s12880-024-01323-3","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T08:02:18Z","timestamp":1718611338000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Robust brain tumor classification by fusion of deep learning and channel-wise attention mode approach"],"prefix":"10.1186","volume":"24","author":[{"given":"Balamurugan","family":"A.G","sequence":"first","affiliation":[]},{"given":"Saravanan","family":"Srinivasan","sequence":"additional","affiliation":[]},{"given":"Preethi","family":"D","sequence":"additional","affiliation":[]},{"given":"Monica","family":"P","sequence":"additional","affiliation":[]},{"given":"Sandeep Kumar","family":"Mathivanan","sequence":"additional","affiliation":[]},{"given":"Mohd Asif","family":"Shah","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,17]]},"reference":[{"key":"1323_CR1","doi-asserted-by":"crossref","unstructured":"Osman \u00d6zkaraca F, Khan J, Hussain J, Khan. 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