An automated metaheuristic-optimized approach for diagnosing and classifying brain tumors based on a convolutional neural network

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102459 - 102459

Published: June 28, 2024

Brain tumors must be classified to determine their severity and appropriate therapy. Applying Artificial Intelligence medical imaging has enabled remarkable developments. The presented framework classifies patients with brain high accuracy efficiency using CNN, pre-trained models, the Manta Ray Foraging Optimization (MRFO) algorithm on X-ray MRI images. Additionally, CNN Transfer Learning (TL) hyperparameters will optimized through MRFO, resulting in improved performance of model. Two public datasets from Kaggle were used build models. first dataset consists two classes, while 2nd includes three contrast-enhanced T1-weighted classes. First, a patient should diagnosed as "Healthy" (or "Tumor"). When scan returns result "Healthy," no abnormalities brain. If reveals that tumor, an performed them. After that, type tumor (i.e., meningioma, pituitary, glioma) identified second proposed classifier. A comparative analysis models two-class showed VGG16's model outperformed other Besides, Xception achieved best results three-class dataset. manual review misclassifications was conducted reasons for correct evaluation suggested architecture yielded 99.96% X-rays 98.64% MRIs. deep learning most current

Language: Английский

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Enhancing breast cancer diagnosis accuracy through genetic algorithm-optimized multilayer perceptron

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2024, Volume and Issue: 7(4), P. 4433 - 4449

Published: June 6, 2024

Language: Английский

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

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 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.

Language: Английский

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Optimizing Tumor Classification Through Transfer Learning and Particle Swarm Optimization-Driven Feature Extraction

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 85929 - 85939

Published: Jan. 1, 2024

Language: Английский

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Optimized brain tumor identification via graph sample and aggregate-attention network with Artificial Lizard Search Algorithm

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 302, P. 112362 - 112362

Published: Aug. 8, 2024

Language: Английский

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ARGUS: Visualization of AI and Machine Learning-Assisted Task Guidance in Augmented Reality

Published: Jan. 1, 2025

Language: Английский

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Lightweight-CancerNet: a deep learning approach for brain tumor detection

PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2670 - e2670

Published: Feb. 21, 2025

Detecting brain tumors in medical imaging is challenging, requiring precise and rapid diagnosis. Deep learning techniques have shown encouraging results this field. However, current models require significant computer resources are computationally demanding. To overcome these constraints, we suggested a new deep architecture named Lightweight-CancerNet, designed to detect efficiently accurately. The proposed framework utilizes MobileNet as the backbone NanoDet primary detection component, resulting notable mean average precision (mAP) of 93.8% an accuracy 98%. In addition, implemented enhancements minimize computing time without compromising accuracy, rendering our model appropriate for real-time object applications. framework's ability with different image distortions has been demonstrated through extensive tests combining two magnetic resonance (MRI) datasets. This research that both resilient reliable. can improve patient outcomes facilitate decision-making surgery while contributing development imaging.

Language: Английский

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Explainable deep stacking ensemble model for accurate and transparent brain tumor diagnosis

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 191, P. 110166 - 110166

Published: April 17, 2025

Early detection of brain tumors in MRI images is vital for improving treatment results. However, deep learning models face challenges like limited dataset diversity, class imbalance, and insufficient interpretability. Most studies rely on small, single-source datasets do not combine different feature extraction techniques better classification. To address these challenges, we propose a robust explainable stacking ensemble model multiclass tumor that combines EfficientNetB0, MobileNetV2, GoogleNet, Multi-level CapsuleNet, using CatBoost as the meta-learner improved aggregation classification accuracy. This approach captures complex characteristics while enhancing robustness The proposed integrates CapsuleNet within framework, utilizing to improve We created two large by merging data from four sources: BraTS, Msoud, Br35H, SARTAJ. tackle applied Borderline-SMOTE augmentation. also utilized methods, along with PCA Gray Wolf Optimization (GWO). Our was validated through confidence interval analysis statistical tests, demonstrating superior performance. Error revealed misclassification trends, assessed computational efficiency regarding inference speed resource usage. achieved 97.81% F1 score 98.75% PR AUC M1, 98.32% 99.34% M2. Moreover, consistently surpassed state-of-the-art CNNs, Vision Transformers, other methods classifying across individual datasets. Finally, developed web-based diagnostic tool enables clinicians interact visualize decision-critical regions scans Explainable Artificial Intelligence (XAI). study connects high-performing AI real clinical applications, providing reliable, scalable, efficient solution

Language: Английский

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Least square-support vector machine based brain tumor classification system with multi model texture features

Frontiers in Applied Mathematics and Statistics, Journal Year: 2023, Volume and Issue: 9

Published: Dec. 6, 2023

Radiologists confront formidable challenges when confronted with the intricate task of classifying brain tumors through analysis MRI images. Our forthcoming manuscript introduces an innovative and highly effective methodology that capitalizes on capabilities Least Squares Support Vector Machines (LS-SVM) in tandem rich insights drawn from Multi-Scale Morphological Texture Features (MMTF) extracted T1-weighted MR underwent meticulous evaluation a substantial dataset encompassing 139 cases, consisting 119 cases aberrant 20 normal The outcomes we achieved are nothing short extraordinary. LS-SVM-based approach vastly outperforms competing classifiers, demonstrating its dominance exceptional accuracy rate 98.97%. This represents 3.97% improvement over alternative methods, accompanied by notable 2.48% enhancement Sensitivity 10% increase Specificity. These results conclusively surpass performance traditional classifiers such as (SVM), Radial Basis Function (RBF), Artificial Neural Networks (ANN) terms classification accuracy. outstanding our model realm tumor diagnosis signifies leap forward field, holding promise delivering more precise dependable tools for radiologists healthcare professionals their pivotal role identifying using imaging techniques.

Language: Английский

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A hybrid encryption approach for efficient and secure data transmission in IoT devices

Journal of Engineering and Applied Science, Journal Year: 2024, Volume and Issue: 71(1)

Published: June 20, 2024

Abstract Security is a crucial concern in the Internet of Things (IoT) ecosystem. Due to IoT devices' constrained processing and storage resources, providing reliable security solutions challenging. Encryption one most commonly used techniques secure user data against unauthorized access. Therefore, it essential develop encryption that have minimal impact on performance devices. This study introduces hybrid approach combines symmetric blowfish with asymmetric elliptic curves. Blowfish encrypt large volumes data, which could otherwise affect execution time. In contrast, curve cryptography utilized ensure private key, has small size does not increase time significantly. The suggested provides advantages both methods, leading an improvement throughput reduction proposed was evaluated, yielding promising results comparison other cryptographic algorithms. show optimization more than 15% efficiency by solution. represents least resources.

Language: Английский

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