Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging

Cancers, Journal Year: 2023, Volume and Issue: 15(16), P. 4172 - 4172

Published: Aug. 18, 2023

The rapid development of abnormal brain cells that characterizes a tumor is major health risk for adults since it can cause severe impairment organ function and even death. These tumors come in wide variety sizes, textures, locations. When trying to locate cancerous tumors, magnetic resonance imaging (MRI) crucial tool. However, detecting manually difficult time-consuming activity might lead inaccuracies. In order solve this, we provide refined You Only Look Once version 7 (YOLOv7) model the accurate detection meningioma, glioma, pituitary gland within an improved system. visual representation MRI scans enhanced by use image enhancement methods apply different filters original pictures. To further improve training our proposed model, data augmentation techniques openly accessible dataset. curated include cases, such as 2548 images gliomas, 2658 pituitary, 2582 2500 non-tumors. We included Convolutional Block Attention Module (CBAM) attention mechanism into YOLOv7 enhance its feature extraction capabilities, allowing better emphasis on salient regions linked with malignancies. model's sensitivity, have added Spatial Pyramid Pooling Fast+ (SPPF+) layer network's core infrastructure. now includes decoupled heads, which allow efficiently glean useful insights from data. addition, Bi-directional Feature Network (BiFPN) used speed up multi-scale fusion collect features associated tumors. outcomes verify efficiency suggested method, achieves higher overall accuracy than previous state-of-the-art models. As result, this framework has lot potential helpful decision-making tool experts field diagnosing

Language: Английский

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A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks

Algorithms, Journal Year: 2023, Volume and Issue: 16(4), P. 176 - 176

Published: March 23, 2023

Creating machines that behave and work in a way similar to humans is the objective of artificial intelligence (AI). In addition pattern recognition, planning, problem-solving, computer activities with include other activities. A group algorithms called “deep learning” used machine learning. With aid magnetic resonance imaging (MRI), deep learning utilized create models for detection categorization brain tumors. This allows quick simple identification Brain disorders are mostly result aberrant cell proliferation, which can harm structure ultimately malignant cancer. The early tumors subsequent appropriate treatment may lower death rate. this study, we suggest convolutional neural network (CNN) architecture efficient using MR images. paper also discusses various such as ResNet-50, VGG16, Inception V3 conducts comparison between proposed these models. To analyze performance models, considered different metrics accuracy, recall, loss, area under curve (AUC). As analyzing our model metrics, concluded performed better than others. Using dataset 3264 images, found CNN had an accuracy 93.3%, AUC 98.43%, recall 91.19%, loss 0.25. We infer reliable variety after comparing it

Language: Английский

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Artificial intelligence assists precision medicine in cancer treatment

Frontiers in Oncology, Journal Year: 2023, Volume and Issue: 12

Published: Jan. 4, 2023

Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of same drugs or surgical methods in patients with tumor may have different curative effects, leading need for more accurate treatment tumors and personalized treatments patients. The precise essential, which renders obtaining an in-depth understanding changes that undergo urgent, including their genes, proteins cancer cell phenotypes, order develop targeted strategies Artificial intelligence (AI) based on big data can extract hidden patterns, important information, corresponding knowledge behind enormous amount data. For example, ML deep learning subsets AI be used mine deep-level information genomics, transcriptomics, proteomics, radiomics, digital pathological images, other data, make clinicians synthetically comprehensively understand tumors. In addition, find new biomarkers from assist screening, detection, diagnosis, prognosis prediction, so as providing best individual improving clinical outcomes.

Language: Английский

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A survey of the vision transformers and their CNN-transformer based variants

Artificial Intelligence Review, Journal Year: 2023, Volume and Issue: 56(S3), P. 2917 - 2970

Published: Oct. 4, 2023

Language: Английский

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PatchResNet: Multiple Patch Division–Based Deep Feature Fusion Framework for Brain Tumor Classification Using MRI Images

Journal of Digital Imaging, Journal Year: 2023, Volume and Issue: 36(3), P. 973 - 987

Published: Feb. 16, 2023

Language: Английский

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Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier

Bioengineering, Journal Year: 2024, Volume and Issue: 11(3), P. 266 - 266

Published: March 8, 2024

There is no doubt that brain tumors are one of the leading causes death in world. A biopsy considered most important procedure cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during treatment, and a lengthy wait for results. Early identification provides patients better prognosis reduces treatment costs. The conventional methods identifying based on medical professional skills, so there possibility human error. labor-intensive nature traditional approaches makes healthcare resources expensive. variety imaging available to detect tumors, magnetic resonance (MRI) computed tomography (CT). Medical research being advanced by computer-aided diagnostic processes enable visualization. Using clustering, automatic tumor segmentation leads accurate detection risk helps effective treatment. This study proposed Fuzzy C-Means algorithm MRI images. To reduce complexity, relevant shape, texture, color features selected. improved Extreme Learning machine classifies 98.56% accuracy, 99.14% precision, 99.25% recall. classifier consistently demonstrates higher accuracy across all classes compared existing models. Specifically, model exhibits improvements ranging from 1.21% 6.23% when other consistent enhancement emphasizes robust performance classifier, suggesting its potential more reliable classification. achieved recall rates 98.47%, 98.59%, 98.74% Fig share dataset 99.42%, 99.75%, 99.28% Kaggle dataset, respectively, which surpasses competing algorithms, particularly detecting glioma grades. shows an improvement approximately 5.39%, 6.22% Despite challenges, artifacts computational study's commitment refining technique addressing limitations positions FCM as noteworthy advancement realm precise efficient identification.

Language: Английский

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Brain Tumor MRI Classification Using a Novel Deep Residual and Regional CNN

Biomedicines, Journal Year: 2024, Volume and Issue: 12(7), P. 1395 - 1395

Published: June 23, 2024

Brain tumor classification is essential for clinical diagnosis and treatment planning. Deep learning models have shown great promise in this task, but they are often challenged by the complex diverse nature of brain tumors. To address challenge, we propose a novel deep residual region-based convolutional neural network (CNN) architecture, called Res-BRNet, using magnetic resonance imaging (MRI) scans. Res-BRNet employs systematic combination regional boundary-based operations within modified spatial blocks. The blocks extract homogeneity, heterogeneity, boundary-related features tumors, while significantly capture local global texture variations. We evaluated performance on challenging dataset collected from Kaggle repositories, Br35H, figshare, containing various categories, including meningioma, glioma, pituitary, healthy images. outperformed standard CNN models, achieving excellent accuracy (98.22%), sensitivity (0.9811), F1-score (0.9841), precision (0.9822). Our results suggest that promising tool classification, with potential to improve efficiency

Language: Английский

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Brain Glial Cell Tumor Classification through Ensemble Deep Learning with APCGAN Augmentation

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 5, 2025

Classification of brain tumor plays a vital role in medical imaging for accurate diagnosis, treatment, and monitoring. Deep learning approaches have gained significant traction this industry because their ability to extract relevant features from images. The research suggests employing an ensemble classifier with weighted voting mechanism categorize glial cell malignancies such as Astrocytoma, Glioblastoma multiforme, Oligodendroglioma, Ependymoma. proposed technique employs three main classifiers: Convolutional Neural Network (CNN), Long Short Term Memory (C-LSTM), + Conditional Random Fields (DCNN+CRF). algorithms require huge amount input data avoid overfitting. Adaptive Progressive Generative Adversarial Networks (APCGANs) are used produce realistic artificial images efficiently train the methodology. Overall, method strategy consistently outperforms other tested (CNN, C-LSTM, DCNN+CRF). Ensemble attained accuracy 99.4 %, recall - 99.1%, precision- 98.0%, F1-score 99.2%. demonstrates superior performance accurately classifying tumors, making it promising algorithm analysis tasks.

Language: Английский

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IoT malware detection architecture using a novel channel boosted and squeezed CNN

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Sept. 15, 2022

Interaction between devices, people, and the Internet has given birth to a new digital communication model, internet of things (IoT). The integration smart devices constitute network introduces many security challenges. These connected have created blind spot, where cybercriminals can easily launch attacks compromise using malware proliferation techniques. Therefore, detection is lifeline for securing IoT against cyberattacks. This study addresses challenge in by proposing CNN-based architecture (iMDA). proposed iMDA modular design that incorporates multiple feature learning schemes blocks including (1) edge exploration smoothing, (2) multi-path dilated convolutional operations, (3) channel squeezing boosting CNN learn diverse set features. local structural variations within classes are learned Edge smoothing operations implemented split-transform-merge (STM) block. operation used recognize global structure patterns. At same time, merging helped regulate complexity get maps. performance evaluated on benchmark dataset compared with several state-of-the architectures. shows promising capacity achieving accuracy: 97.93%, F1-Score: 0.9394, precision: 0.9864, MCC: 0. 8796, recall: 0.8873, AUC-PR: 0.9689 AUC-ROC: 0.9938. strong discrimination suggests may be extended android-based Elf files compositely future.

Language: Английский

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Brain tumor classification utilizing deep features derived from high-quality regions in MRI images

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 85, P. 104988 - 104988

Published: May 9, 2023

Language: Английский

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