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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Detection of brain tumors using a transfer learning-based optimized ResNet152 model in MR images

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 188, P. 109790 - 109790

Published: Feb. 13, 2025

Language: Английский

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Trade-off between training and testing ratio in machine learning for medical image processing

PeerJ Computer Science, Journal Year: 2024, Volume and Issue: 10, P. e2245 - e2245

Published: Sept. 6, 2024

Artificial intelligence (AI) and machine learning (ML) aim to mimic human enhance decision making processes across various fields. A key performance determinant in a ML model is the ratio between training testing dataset. This research investigates impact of varying train-test split ratios on generalization capabilities using BraTS 2013 Logistic regression, random forest, k nearest neighbors, support vector machines were trained with ranging from 60:40 95:05. Findings reveal significant variations accuracies these ratios, emphasizing critical need strike balance avoid overfitting or underfitting. The study underscores importance selecting an optimal that considers tradeoffs such as metrics, statistical measures, resource constraints. Ultimately, insights contribute deeper understanding how selection impacts effectiveness reliability applications diverse

Language: Английский

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Improved EfficientNet Architecture for Multi-Grade Brain Tumor Detection

Electronics, Journal Year: 2025, Volume and Issue: 14(4), P. 710 - 710

Published: Feb. 12, 2025

Accurate detection and diagnosis of brain tumors at early stages is significant for effective treatment. While numerous methods have been developed tumor classification, several rely on traditional techniques, often resulting in suboptimal performance. In contrast, AI-based deep learning techniques shown promising results, consistently achieving high accuracy across various types while maintaining model interpretability. Inspired by these advancements, this paper introduces an improved variant EfficientNet multi-grade addressing the gap between performance explainability. Our approach extends capabilities to classify four types: glioma, meningioma, pituitary tumor, non-tumor. For enhanced explainability, we incorporate gradient-weighted class activation mapping (Grad-CAM) improve The input MRI images undergo data augmentation before being passed through feature extraction phase, where underlying patterns are learned. achieves average 98.6%, surpassing other state-of-the-art standard datasets a substantially reduced parameter count. Furthermore, explainable AI (XAI) analysis demonstrates model’s ability focus relevant regions, enhancing its This accurate interpretable classification has potential significantly aid clinical decision-making neuro-oncology.

Language: Английский

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An Efficient Brain Tumor Detection and Classification using Pre-Trained Convolutional Neural Network Models

Heliyon, Journal Year: 2024, Volume and Issue: 10(17), P. e36773 - e36773

Published: Aug. 26, 2024

In cases of brain tumors, some cells experience abnormal and rapid growth, leading to the development tumors. Brain tumors represent a significant source illness affecting brain. Magnetic Resonance Imaging (MRI) stands as well-established coherent diagnostic method for cancer detection. However, resulting MRI scans produce vast number images, which require thorough examination by radiologists. Manual assessment these images consumes considerable time may result in inaccuracies Recently, deep learning has emerged reliable tool decision-making tasks across various domains, including finance, medicine, cybersecurity, agriculture, forensics. context diagnosis, Deep Learning Machine algorithms applied data enable prognosis. achieving higher accuracy is crucial providing appropriate treatment patients facilitating prompt To address this, we propose use Convolutional Neural Networks (CNN) tumor Our approach utilizes dataset consisting two classes: three representing different types one non-tumor samples. We present model that leverages pre-trained CNNs categorize cases. Additionally, augmentation techniques are employed augment size. The effectiveness our proposed CNN evaluated through metrics, validation loss, confusion matrix, overall loss. employing ResNet50 EfficientNet demonstrated levels accuracy, precision, recall detecting

Language: Английский

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Artificial intelligence‐driven sustainability: Enhancing carbon capture for sustainable development goals– A review

Sustainable Development, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 6, 2024

Abstract Artificial intelligence (AI) and environmental points are equally important components within the response to local weather change. Therefore, based on efforts of reducing carbon emissions more efficiently effectively, this study tries focus AI integration with capture technology. The urgency tackling climate change means we need advanced capture, is an area where can make a huge impact in how these technologies operated managed. It will minimize manufacturing improve both resource efficiency as well our planet's footprint by turning waste into something value again. could be leveraged analyze data sets from plants, searching for optimal system settings efficient ways identifying patterns available information at larger scale than currently possible. In addition, incorporated sensors monitoring mechanisms supply chain identify any operational failure reception itself allowing timely action protect those areas. also helps generative design materials, which allows researchers explore new types carbon‐absorbing material, including metal–organic frameworks polymeric materials that industrial CO 2 , such moisture. it increases accuracy reservoir simulations controls injection systems storage or enhanced oil recovery. Through applying algorithms geology, production performance real‐time would like facilitate optimization processes while assuring maximum efficiency. integrates renewable‐based employed AI‐driven smart grid methods.

Language: Английский

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Transformative Advances in AI for Precise Cancer Detection: A Comprehensive Review of Non-Invasive Techniques

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 11, 2025

Language: Английский

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Advancing Medical Imaging with Capsule Networks for Diagnostic Accuracy

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: April 30, 2025

The use of capsule networks into medical imaging as a means advancing is possible path for improving diagnostic accuracy. objective this study to enhance the interpretation and categorization pictures by making hierarchical pose-sensitive representations that are made available Capsule Networks. purpose project improve capability machine learning models reliably identify categorize abnormalities, lesions, other pathological findings in data. This will be accomplished capturing detailed spatial connections including perspective invariance. major goal doctors' early diagnosis abilities patient outcomes treatment times. When it comes situations which typical convolutional neural could have difficulty dealing with complicated structures or changes position appearance, method very helpful. Networks potential diagnostics offering interpretable contextually rich representations. would enable physicians access technologies more reliable efficient illness identification diagnosis.

Language: 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, Journal Year: 2025, Volume and Issue: 186, P. 109703 - 109703

Published: Jan. 24, 2025

Language: Английский

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A novel similarity navigated graph neural networks and crayfish optimization algorithm for accurate brain tumor detection

Research on Biomedical Engineering, Journal Year: 2025, Volume and Issue: 41(2)

Published: April 5, 2025

Language: Английский

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