Classifying Brain Tumors on Magnetic Resonance Imaging by Using Convolutional Neural Networks

Electronics, Год журнала: 2023, Номер 12(4), С. 955 - 955

Опубликована: Фев. 14, 2023

The study of neuroimaging is a very important tool in the diagnosis central nervous system tumors. This paper presents evaluation seven deep convolutional neural network (CNN) models for task brain tumor classification. A generic CNN model implemented and six pre-trained are studied. For this proposal, dataset utilized Msoud, which includes Fighshare, SARTAJ, Br35H datasets, containing 7023 MRI images. magnetic resonance imaging (MRI) belongs to four classes, three tumors, including Glioma, Meningioma, Pituitary, one class healthy brains. trained with input images several preprocessing strategies applied paper. evaluated Generic CNN, ResNet50, InceptionV3, InceptionResNetV2, Xception, MobileNetV2, EfficientNetB0. In comparison all models, best was obtained an average Accuracy 97.12%. development these techniques could help clinicians specializing early detection

Язык: Английский

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Brain Tumor Detection and Multi-Grade Segmentation Through Hybrid Caps-VGGNet Model

IEEE Access, Год журнала: 2023, Номер 11, С. 72518 - 72536

Опубликована: Янв. 1, 2023

Around the world, brain tumors are becoming leading cause of mortality. The inability to undertake a timely tumor diagnosis is primary this pandemic. Brain cancer crucial procedure that relies on expertise and experience doctor. Radiologists must use an automated classification model find cancers. current model's accuracy has be improved get suitable therapies. can consult various computer-aided diagnostic (CAD) models in literature medical imaging assist them with their patients. Previous research widely used CNN for detection classification, which typically require large datasets. This proposed Caps-VGGNet hybrid model, integrates CapsNet VGGNet by adding layers VGGNet. presented addresses challenge requiring datasets automatically extracting classifying features. suggested algorithm's effectiveness was assessed using Brats-2020 Brats-2019 dataset, contains high-quality images tumors. Compared other conventional models, empirical outcomes indicate it exhibited highest level superior efficacy terms accuracy, specificity, sensitivity. Specifically, attained 0.99, specificity sensitivity 0.98 Brats20 dataset.

Язык: Английский

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Detection of MRI brain tumor using residual skip block based modified MobileNet model

Cluster Computing, Год журнала: 2025, Номер 28(4)

Опубликована: Фев. 25, 2025

Язык: Английский

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Development of hybrid models based on deep learning and optimized machine learning algorithms for brain tumor Multi-Classification

Expert Systems with Applications, Год журнала: 2023, Номер 238, С. 122159 - 122159

Опубликована: Окт. 18, 2023

Язык: Английский

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Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification

Cancers, Год журнала: 2023, Номер 15(6), С. 1767 - 1767

Опубликована: Март 14, 2023

Brain tumors and other nervous system cancers are among the top ten leading fatal diseases. The effective treatment of brain depends on their early detection. This research work makes use 13 features with a voting classifier that combines logistic regression stochastic gradient descent using extracted by deep convolutional layers for efficient classification tumorous victims from normal. From first second-order tumor features, model training. Using helps to increase precision non-tumor patient classification. proposed along convoluted produces results show highest accuracy 99.9%. Compared cutting-edge methods, approach has demonstrated improved accuracy.

Язык: Английский

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Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models

Cancers, Год журнала: 2025, Номер 17(1), С. 121 - 121

Опубликована: Янв. 2, 2025

Background/Objectives: Brain tumor classification is a crucial task in medical diagnostics, as early and accurate detection can significantly improve patient outcomes. This study investigates the effectiveness of pre-trained deep learning models classifying brain MRI images into four categories: Glioma, Meningioma, Pituitary, No Tumor, aiming to enhance diagnostic process through automation. Methods: A publicly available Tumor dataset containing 7023 was used this research. The employs state-of-the-art models, including Xception, MobileNetV2, InceptionV3, ResNet50, VGG16, DenseNet121, which are fine-tuned using transfer learning, combination with advanced preprocessing data augmentation techniques. Transfer applied fine-tune optimize accuracy while minimizing computational requirements, ensuring efficiency real-world applications. Results: Among tested Xception emerged top performer, achieving weighted 98.73% F1 score 95.29%, demonstrating exceptional generalization capabilities. These proved particularly effective addressing class imbalances delivering consistent performance across various evaluation metrics, thus their suitability for clinical adoption. However, challenges persist improving recall Glioma Meningioma categories, black-box nature requires further attention interpretability trust settings. Conclusions: findings underscore transformative potential imaging, offering pathway toward more reliable, scalable, efficient tools. Future research will focus on expanding diversity, model explainability, validating settings support widespread adoption AI-driven systems healthcare ensure integration workflows.

Язык: Английский

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VITALT: a robust and efficient brain tumor detection system using vision transformer with attention and linear transformation

Neural Computing and Applications, Год журнала: 2024, Номер 36(12), С. 6403 - 6419

Опубликована: Фев. 22, 2024

Язык: Английский

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Explainable ensemble deep learning-based model for brain tumor detection and classification

Neural Computing and Applications, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 20, 2024

Abstract Brain tumors are very dangerous as they cause death. A lot of people die every year because brain tumors. Therefore, accurate classification and detection in the early stages can help recovery. Various deep learning techniques have achieved good results tumor classification. The traditional methods training neural network from scratch time-consuming last for weeks training. this work, we proposed an ensemble approach depending on transfer that utilizes pre-trained models DenseNet121 InceptionV3 to detect three forms tumors: meningioma, glioma, pituitary. While developing model, some changes were made architecture by replacing their classifiers (fully connected SoftMax layers) with a new classifier adopt recent task. In addition, gradient-weighted class activation maps (Grad-CAM) explainable model verify achieve high confidence. suggested was validated using publicly available dataset 99.02% accuracy, 98.75% precision, 98.98% recall, 98.86% F1 score. outperformed others detecting classifying MRI data, verifying degree trust.

Язык: Английский

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Deep Feature Extraction with Cubic-SVM for Classification of Brain Tumor

STUDIES IN ENGINEERING AND EXACT SCIENCES, Год журнала: 2024, Номер 5(1), С. 19 - 35

Опубликована: Янв. 12, 2024

Brain tumors (BT) are fatal and debilitating conditions that shorten the typical lifespan of patients. Patients with BTs who receive inadequate treatment an incorrect diagnosis have a lower chance survival. Magnetic resonance imaging (MRI) is often employed to assess tumor. However, because massive quantity data provided by MRI, early BT detection complex time-consuming procedure in biomedical imaging. As consequence, automated efficient strategy required. The brain or malignancies has been done using variety conventional machine learning (ML) approaches. manually collected properties, however, provide main problem these models. constraints previously stated addressed fusion deep model for binary classification presented this study. recommended method combines two different CNN (Efficientnetb0, VGG-19) models automatically extract features make use feature’s Cubic SVM classifier model. Additionally, approach displayed outstanding performance various measures, including Accuracy (99.78%), Precision Recall F1-Score on same Kaggle (Br35H) dataset. proposed performs better than current approaches classifying from MRI images.

Язык: Английский

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YOLO-TumorNet: An innovative model for enhancing brain tumor detection performance

Alexandria Engineering Journal, Год журнала: 2025, Номер 119, С. 211 - 221

Опубликована: Фев. 5, 2025

Язык: Английский

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