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

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(8), P. 11187 - 11212

Published: May 20, 2024

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

Language: Английский

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NeuroSight: A Deep‐Learning Integrated Efficient Approach to Brain Tumor Detection

Engineering Reports, Journal Year: 2025, Volume and Issue: 7(1)

Published: Jan. 1, 2025

ABSTRACT Brain tumors pose a significant health risk and require immediate attention. Despite progress, accurately classifying these remains challenging due to their location, shape, size variability. This has led exploring deep learning machine in biomedical imaging, particularly processing analyzing Magnetic Resonance Imaging (MRI) data. study compared newly developed Convolutional Neural Network model pre‐trained models using transfer learning, focusing on comprehensive comparison involving VGG‐16, ResNet‐50, AlexNet, Inception‐v3. VGG‐16 outperformed all other with 95.52% test accuracy, 99.87% training 0.2348 validation loss. ResNet‐50 got 93.31% 98.78% 0.6327 The CNN 0.2960 loss, 92.59% 98.11% accuracy. worst seemed be Inception‐v3, 89.40% 97.89% 0.4418 approach facilitates deep‐learning researchers identifying categorizing brain cancers by comparing recent papers assessing methodologies.

Language: Английский

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Brain tumor detection and classification in MRI using hybrid ViT and GRU model with explainable AI in Southern Bangladesh

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 1, 2024

Language: Английский

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Brain tumour histopathology through the lens of deep learning: A systematic review

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 186, P. 109642 - 109642

Published: Jan. 8, 2025

Language: Английский

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A prior knowledge-guided distributionally robust optimization-based adversarial training strategy for medical image classification

Information Sciences, Journal Year: 2024, Volume and Issue: 673, P. 120705 - 120705

Published: May 7, 2024

Language: Английский

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USING CONVOLUTIONAL NETWORK IN GRAPHICAL MODEL DETECTION OF AUTISM DISORDERS WITH FUZZY INFERENCE SYSTEMS

Intelligence-Based Medicine, Journal Year: 2025, Volume and Issue: 11, P. 100213 - 100213

Published: Jan. 1, 2025

Language: Английский

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Early brain stroke detection using multilayer perceptron of convolutional neural network-based residual network

Technology and Health Care, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

Background Stroke, medically known as the brain attack, refers to stoppage or of blood from flowing into a particular region brain, even breaking vessel, causing injury and death areas brain. It presents medical emergency, with potential severe long-term neurological impairment, disability, death; thus, urgent detection treatment are needed. Objective The study aims develop novel Multilayer Perceptron Convolutional Neural Network-based Residual Network (MLPCNNbRN) for early stroke detection, focusing on improving accuracy reliability detecting subtle patterns in images. Methods MLPCNNbRN provided resented context residual connections within an architecture designed deep network training This allowed overall model learn complex relations very effectively. system was implemented Python framework. Its performance compared other methods. key metrics used evaluation were accuracy, precision, recall, F-score. Results demonstrated superior existing methods, achieving higher levels detection. Specifically, improved F-score, showcasing its robustness identifying patterns. Conclusion proposed enhances by extracting hierarchical features learning, offering more accurate reliable approach than previous has aid professionals timely diagnosis treatment, ultimately patient outcomes.

Language: Английский

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AITL-Net: An Adaptive Interpretable Transfer Learning Network with Robust Generalization for Liver Cancer Recognition

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113473 - 113473

Published: April 1, 2025

Language: Английский

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AI in MRI Brain Tumor Diagnosis: A Systematic Review of Machine Learning and Deep Learning Advances (2010–2025)

Chemometrics and Intelligent Laboratory Systems, Journal Year: 2025, Volume and Issue: 263, P. 105414 - 105414

Published: April 25, 2025

Language: Английский

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Advancements in Brain Tumour Analysis: A Review of Machine Learning, Deep Learning, Image Processing, and Explainable AI Techniques

Operations Research Forum, Journal Year: 2025, Volume and Issue: 6(2)

Published: May 5, 2025

Language: Английский

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