Deep learning for multi-grade brain tumor detection and classification: a prospective survey

Bhagyalaxmi, K.; Dwarakanath, B.; Reddy, P. Vijaya Pal

doi:10.1007/s11042-024-18129-8

Cited by 6 publications

(2 citation statements)

References 65 publications

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“…Numerous scientific studies focus on brain tumor diagnosis, revealing that deep learning has led to significant advancements in this area [9,[33][34][35][36][37]. Deep learning has become a groundbreaking method, offering crucial benefits for accurately diagnosing brain tumors due to their complexity.…”

Section: Introductionmentioning

confidence: 99%

“…Research indicates that deep learning has evolved into an innovative and impactful approach in this field. Bhagyalaxmi et al [36] found that deep learning, particularly CNNs, shows promise for brain tumor detection. While their review outlines current techniques, they suggest future research explore hybrid models combining deep learning with other methodologies to maximize effectiveness.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification

Pacal,

Celik,

Bayram

et al. 2024

Cluster Comput

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The early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination of such conditions. Despite the advancements in Computer-Aided Diagnosis (CADx) systems powered by deep learning, the challenge of accurately classifying brain tumors from MRI scans persists due to the high variability of tumor appearances and the subtlety of early-stage manifestations. This work introduces a novel adaptation of the EfficientNetv2 architecture, enhanced with Global Attention Mechanism (GAM) and Efficient Channel Attention (ECA), aimed at overcoming these hurdles. This enhancement not only amplifies the model’s ability to focus on salient features within complex MRI images but also significantly improves the classification accuracy of brain tumors. Our approach distinguishes itself by meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance in detecting a broad spectrum of brain tumors. Demonstrated through extensive experiments on a large public dataset, our model achieves an exceptional high-test accuracy of 99.76%, setting a new benchmark in MRI-based brain tumor classification. Moreover, the incorporation of Grad-CAM visualization techniques sheds light on the model’s decision-making process, offering transparent and interpretable insights that are invaluable for clinical assessment. By addressing the limitations inherent in previous models, this study not only advances the field of medical imaging analysis but also highlights the pivotal role of attention mechanisms in enhancing the interpretability and accuracy of deep learning models for brain tumor diagnosis. This research sets the stage for advanced CADx systems, enhancing patient care and treatment outcomes.

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