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

---

Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 3, 2025

Detecting brain tumours (BT) early improves treatment possibilities and increases patient survival rates. Magnetic resonance imaging (MRI) scanning offers more comprehensive information, such as better contrast clarity, than any alternative process. Manually separating BTs from several MRI images gathered in medical practice for cancer analysis is challenging time-consuming. Tumours scans of the are exposed utilizing methods machine learning technologies, simplifying process doctors. can sometimes appear normal even when a has tumour or malignancy. Deep approaches have recently depended on deep convolutional neural networks to analyze with promising outcomes. It supports saving lives faster rectifying some errors. With this motivation, article presents new explainable artificial intelligence semantic segmentation Bayesian tumors (XAISS-BMLBT) technique. The presented XAISS-BMLBT technique mainly concentrates classification BT images. approach initially involves bilateral filtering-based image pre-processing eliminate noise. Next, performs MEDU-Net+ define impacted regions. For feature extraction process, ResNet50 model utilized. Furthermore, regularized network (BRANN) used identify presence BTs. Finally, an improved radial movement optimization employed hyperparameter tuning BRANN To highlight performance technique, series simulations were accomplished by benchmark database. experimental validation portrayed superior accuracy value 97.75% over existing models.

Language: Английский

---

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, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 28, 2025

Language: Английский

---

Multimodal brain tumor segmentation and classification from MRI scans based on optimized DeepLabV3+ and interpreted networks information fusion empowered with explainable AI

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 182, P. 109183 - 109183

Published: Oct. 2, 2024

Language: Английский

---

A Novel Ensemble Framework for Multi-Classification of Brain Tumors Using Magnetic Resonance Imaging

Diagnostics, Journal Year: 2024, Volume and Issue: 14(4), P. 383 - 383

Published: Feb. 9, 2024

Brain tumors can have fatal consequences, affecting many body functions. For this reason, it is essential to detect brain tumor types accurately and at an early stage start the appropriate treatment process. Although convolutional neural networks (CNNs) are widely used in disease detection from medical images, they face problem of overfitting training phase on limited labeled insufficiently diverse datasets. The existing studies use transfer learning ensemble models overcome these problems. When examined, evident that there a lack weight ratios will be with technique. With framework proposed study, several CNN different architectures trained fine-tuning three A particle swarm optimization-based algorithm determined optimum weights for combining five most successful results across datasets as follows: Dataset 1, 99.35% accuracy 99.20 F1-score; 2, 98.77% 98.92 3, 99.92% 99.92 F1-score. We achieved performances datasets, showing reliable classification. As result, outperforms studies, offering clinicians enhanced decision-making support through its high-accuracy classification performance.

Language: Английский

---

Diffusion model-based text-guided enhancement network for medical image segmentation

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 249, P. 123549 - 123549

Published: Feb. 20, 2024

Language: Английский

---

Neuro-XAI: Explainable deep learning framework based on deeplabV3+ and bayesian optimization for segmentation and classification of brain tumor in MRI scans

Journal of Neuroscience Methods, Journal Year: 2024, Volume and Issue: 410, P. 110247 - 110247

Published: Aug. 10, 2024

Language: Английский

---

Modified U-Net with attention gate for enhanced automated brain tumor segmentation

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 2, 2025

Language: Английский

---

Utilizing customized CNN for brain tumor prediction with explainable AI

Heliyon, Journal Year: 2024, Volume and Issue: 10(20), P. e38997 - e38997

Published: Oct. 1, 2024

Timely diagnosis of brain tumors using MRI and its potential impact on patient survival are critical issues addressed in this study. Traditional DL models often lack transparency, leading to skepticism among medical experts owing their "black box" nature. This study addresses gap by presenting an innovative approach for tumor detection. It utilizes a customized Convolutional Neural Network (CNN) model empowered three advanced explainable artificial intelligence (XAI) techniques: Shapley Additive Explana-tions (SHAP), Local Interpretable Model-agnostic Explanations (LIME), Gradient-weighted Class Activation Mapping (Grad-CAM). The utilized the BR35H dataset, which includes 3060 images encompassing both tumorous non-tumorous cases. proposed achieved remarkable training accuracy 100 % validation 98.67 %. Precision, recall, F1 score metrics demonstrated exceptional performance at 98.50 %, confirming Detailed result analysis, including confusion matrix, comparison with existing models, generalizability tests other datasets, establishes superiority sets new benchmark accuracy. By integrating CNN XAI techniques, research enhances trust AI-driven diagnostics offers promising pathway early detection potentially life-saving interventions.

Language: Английский

---

Dynamic Focus on Tumor Boundaries: A Lightweight U-Net for MRI Brain Tumor Segmentation

Bioengineering, Journal Year: 2024, Volume and Issue: 11(12), P. 1302 - 1302

Published: Dec. 23, 2024

Accurate segmentation of brain tumors in MRI scans is critical for diagnosis and treatment planning. Traditional models, such as U-Net, excel capturing spatial information but often struggle with complex tumor boundaries subtle variations image contrast. These limitations can lead to inconsistencies identifying regions, impacting the accuracy clinical outcomes. To address these challenges, this paper proposes a novel modification U-Net architecture by integrating attention mechanism designed dynamically focus on relevant regions within scans. This innovation enhances model's ability delineate fine improves precision. Our model was evaluated Figshare dataset, which includes annotated images meningioma, glioma, pituitary tumors. The proposed achieved Dice similarity coefficient (DSC) 0.93, recall 0.95, an AUC 0.94, outperforming existing approaches V-Net, DeepLab V3+, nnU-Net. results demonstrate effectiveness our addressing key challenges like low-contrast boundaries, small overlapping Furthermore, lightweight design ensures its suitability real-time applications, making it robust tool automated segmentation. study underscores potential mechanisms significantly enhance medical imaging models paves way more effective diagnostic tools.

Language: Английский

---