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

Cluster Computing, Год журнала: 2024, Номер 27(8), С. 11187 - 11212

Опубликована: Май 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.

Язык: Английский

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Enhanced brain tumor detection and classification using a deep image recognition generative adversarial network (DIR-GAN): a comparative study on MRI, X-ray, and FigShare datasets

Neural Computing and Applications, Год журнала: 2025, Номер unknown

Опубликована: Фев. 28, 2025

Язык: Английский

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Improved Brain Tumor Segmentation Using Modified U-Net based on Particle Swarm Optimization Image Enhancement

Proceedings of the Genetic and Evolutionary Computation Conference Companion, Год журнала: 2024, Номер unknown, С. 611 - 614

Опубликована: Июль 14, 2024

Язык: Английский

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Automated multi-class MRI brain tumor classification and segmentation using deformable attention and saliency mapping

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 8, 2025

In the diagnosis and treatment of brain tumors, automatic classification segmentation medical images play a pivotal role. Early detection facilitates timely intervention, significantly improving patient survival rates. This study introduces novel method for automated aiming to enhance both diagnostic accuracy efficiency. Magnetic Resonance (MR) imaging remains gold standard in clinical tumor diagnostics; however, it is time-intensive labor-intensive process. Consequently, integration detection, localization, methods not only desirable but essential. this research, we present framework that enables post-classification feature extraction, allowing first-time multiple types. To improve characterization, applied data augmentation techniques MR developed hierarchical multiscale deformable attention module (MS-DAM). model effectively captures irregular complex patterns, enhancing performance. Following classification, comprehensive process was conducted across large dataset, reinforcing model's role as decision support system. Utilizing Kaggle dataset containing 14 different types with highly similar morphologic structures, validated proposed efficacy. Compared existing multi-scale channel modules, MS-DAM achieved superior accuracy, exceeding 96.5%. presents promising approach tumors imaging, offering significant advancements clinics paving way more efficient, accurate, scalable methodologies.

Язык: Английский

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CFNet: Cross-scale fusion network for medical image segmentation

Journal of King Saud University - Computer and Information Sciences, Год журнала: 2024, Номер 36(7), С. 102123 - 102123

Опубликована: Июль 11, 2024

Learning multi-scale feature representations is essential for medical image segmentation. Most existing frameworks are based on U-shape architecture in which the high-resolution representation recovered progressively by connecting different levels of decoder with low-resolution from encoder. However, intrinsic defects complementary fusion inhibit aggregating efficient global and discriminative features along object boundaries. While Transformer can help model features, their computation complexity limits application real-time scenarios. To address these issues, we propose a Cross-scale Fusion Network (CFNet), combining cross-scale attention module pyramidal to fuse multi-stage/global context information. Specifically, first utilize large kernel convolution design basic building block capable extracting local Then, Bidirectional Atrous Spatial Pyramid Pooling (BiASPP), employs atrous bidirectional paths capture various shapes sizes brain tumors. Furthermore, introduce cross-stage mechanism reduce redundant information when merging two stages semantics. Extensive evaluation was performed five segmentation datasets: 3D volumetric dataset, namely Brats benchmarks. CFNet-L achieves 85.74% 90.98% dice score Enhanced Tumor Whole Brats2018, respectively. our largest outperformed other methods 2D image. It achieved 71.95%, 82.79%, 80.79% SE STARE, DRIVE, CHASEDB1, The code will be available at https://github.com/aminabenabid/CFNet

Язык: Английский

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Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning

Computers in Biology and Medicine, Год журнала: 2025, Номер 186, С. 109703 - 109703

Опубликована: Янв. 24, 2025

Язык: Английский

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A synaptic deep tumor sense predictor system for brain tumor detection and classification

Alexandria Engineering Journal, Год журнала: 2025, Номер 123, С. 29 - 45

Опубликована: Март 23, 2025

Язык: Английский

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Deep Learning Driven Brain Tumor Detection With CNN-Based MRI Analysis With Better Accuracy and Precision

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 121 - 142

Опубликована: Май 9, 2025

The proposed research focuses on how an early and reliable brain tumor diagnosis by MRI can help the patient through timely intervention, which is of utmost importance. Currently, radiologists conduct a manual interpretation images, lengthy process usually accompanied failure to detect small complex tumors in large dataset. To improve detection, this work applies deep learning models with CNN based neural network for more accurate diagnosis. Sufficient preprocessing techniques are utilized reduce noise enhance contrast within region where spots located but without important details being ignored. Future efforts involve modifying structure investigating possibility transfer better performance different datasets. This aims quality automated medical imaging systems would have overturned effect outcomes while assisting development faster, reliable, precise diagnostic tools.

Язык: Английский

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A Comparative Study of Deep Learning Models for Brain Tumor Classification

Опубликована: Июнь 21, 2024

Язык: Английский

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An Adapted Convolutional Neural Network for Brain Tumor Detection

Open Journal of Applied Sciences, Год журнала: 2024, Номер 14(10), С. 2809 - 2825

Опубликована: Янв. 1, 2024

Язык: Английский

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