Artificial Intelligence With Neural Network Algorithms in Pediatric Astrocytoma Diagnosis: A Systematic Review

Innovative medicine of Kuban, Journal Year: 2025, Volume and Issue: 10(1), P. 93 - 100

Published: Feb. 26, 2025

Background: Astrocytoma is a common pediatric brain tumor that poses significant health burden. Recent advancements in artificial intelligence (AI), particularly neural network algorithms, have been studied for their precision and efficiency medical diagnostics via effectively analyzing imaging data to identify patterns anomalies. Objective: To systematically review AI-based diagnostic tools with algorithms’ methodologies, sensitivities, specificities, potential clinical integration astrocytoma, providing consolidated perspective on overall performance impact decision-making. Methods: As per PRISMA 2020 guidelines, we conducted comprehensive search PubMed, Scopus, ScienceDirect February 5, 2024. The strategy was guided by PECO question focusing astrocytoma diagnosis using AI algorithms vs computed tomography or magnetic resonance (MRI). Keywords were terms related algorithms. We included studies the accuracy of methods cases (World Health Organization grades 1-3), no restrictions publication year country. excluded papers written languages other than English Bahasa Indonesia nonhuman studies. Data assessed Effective Public Practice Project tool. Results: Of 454 articles screened, 6 met inclusion criteria. These varied design, location, sample size, ranging from 10 135 subjects. showed high sensitivity specificity, often surpassing traditional radiological techniques. Notably, 3-dimensional MRI demonstrated improved compared 2-dimensional (96% 77%). models exhibited levels comparable exceeding expert radiologists, metrics such as classification 92% values area under receiver operating characteristic curve. Conclusions: shows promise enhancing diagnosis. reviewed indicate these advanced can achieve superior specificity conventional Integrating into practice could substantially improve patient outcomes.

Language: Английский

---

PCG-CAM: Enhanced class activation map using principal component of gradient and its applications in brain MRI

Information Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 122046 - 122046

Published: March 1, 2025

Language: Английский

---

Nanophotonic-enhanced photoacoustic imaging for brain tumor detection

Journal of Nanobiotechnology, Journal Year: 2025, Volume and Issue: 23(1)

Published: March 5, 2025

Photoacoustic brain imaging (PABI) has emerged as a promising biomedical modality, combining high contrast of optical with deep tissue penetration ultrasound imaging. This review explores the application photoacoustic in tumor imaging, highlighting synergy between nanomaterials and state art techniques to achieve high-resolution deeper tissues. PABI leverages effect, where absorbed light energy causes thermoelastic expansion, generating waves that are detected converted into images. technique enables precise diagnosis, therapy monitoring, enhanced clinical screening, specifically management complex diseases such breast cancer, lymphatic disorder, neurological conditions. Despite integration agents radiation, providing comprehensive overview current methodologies, major obstacles treatment, future directions for improving diagnostic therapeutic outcomes. The underscores significance robust research tool medical method, potential revolutionize disease diagnosis treatment.

Language: Английский

---

3D AIR-UNet: attention–inception–residual-based U-Net for brain tumor segmentation from multimodal MRI

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

Published: March 10, 2025

Language: Английский

---

AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 13, 2025

Cancer remains a significant health issue, resulting in around 10 million deaths per year, particularly developing nations. Demographic changes, socio-economic variables, and lifestyle choices are responsible for the rise cancer cases. Despite potential to mitigate adverse effects of by early detection implementation prevention methods, several nations have limited screening facilities. In oncology, use artificial intelligence (AI) represents transformative advancement diagnosis, prognosis, treatment. The AI biomarker discovery improves precision medicine uncovering signatures that essential treatment diseases within vast diverse datasets. Deep learning machine diagnostics two examples technologies changing way biomarkers made finding patterns large datasets making new make it possible deliver accurate effective therapies. Existing gaps include data quality, algorithmic transparency, ethical concerns privacy, among others. methodologies with seeks transform improving patient survival rates through enhanced diagnosis targeted therapy. This commentary aims clarify how is identification novel optimal focused treatment, improved clinical outcomes, while also addressing certain obstacles issues related application oncology. Data from reputable scientific databases such as PubMed, Scopus, ScienceDirect were utilized.

Language: Английский

---

Comprehensive Survey on Computational Techniques for Brain Tumor Detection: Past, Present and Future

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

Published: March 18, 2025

Language: Английский

---

Brain tumor segmentation using multi-scale attention U-Net with EfficientNetB4 encoder for enhanced MRI analysis

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

Published: March 22, 2025

Abstract Accurate brain tumor segmentation is critical for clinical diagnosis and treatment planning. This study proposes an advanced framework that combines Multiscale Attention U-Net with the EfficientNetB4 encoder to enhance performance. Unlike conventional U-Net-based architectures, proposed model leverages EfficientNetB4’s compound scaling optimize feature extraction at multiple resolutions while maintaining low computational overhead. Additionally, Multi-Scale Mechanism (utilizing $$1\times 1, 3\times 3$$ , $$5\times 5$$ kernels) enhances representation by capturing boundaries across different scales, addressing limitations of existing CNN-based methods. Our approach effectively suppresses irrelevant regions localization through attention-enhanced skip connections residual attention blocks. Extensive experiments were conducted on publicly available Figshare dataset, comparing EfficientNet variants determine optimal architecture. demonstrated superior performance, achieving Accuracy 99.79%, MCR 0.21%, Dice Coefficient 0.9339, Intersection over Union (IoU) 0.8795, outperforming other in accuracy efficiency. The training process was analyzed using key metrics, including Coefficient, dice loss, precision, recall, specificity, IoU, showing stable convergence generalization. method evaluated against state-of-the-art approaches, surpassing them all accuracy, mean IoU. demonstrates effectiveness robust efficient tumors, positioning it as a valuable tool research applications.

Language: Английский

---

EC-HDLNet: Extended coati-based hybrid deep dilated convolutional learning network for brain tumor classification

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 107, P. 107865 - 107865

Published: March 26, 2025

Language: Английский

---

Dual-Stream Contrastive Latent Learning Generative Adversarial Network for Brain Image Synthesis and Tumor Classification

Journal of Imaging, Journal Year: 2025, Volume and Issue: 11(4), P. 101 - 101

Published: March 28, 2025

Generative adversarial networks (GANs) prioritize pixel-level attributes over capturing the entire image distribution, which is critical in synthesis. To address this challenge, we propose a dual-stream contrastive latent projection generative network (DSCLPGAN) for robust augmentation of MRI images. The generator our architecture incorporates two specialized processing pathways: one dedicated to local feature variation modeling, while other captures global structural transformations, ensuring more comprehensive synthesis medical We used transformer-based encoder-decoder framework contextual coherence and learning (CLP) module integrates loss into space generating diverse samples. generated images undergo refinement using an ensemble discriminators, where discriminator 1 (D1) ensures classification consistency with real images, 2 (D2) produces probability map localized variations, 3 (D3) preserves consistency. For validation, utilized publicly available dataset contains 3064 T1-weighted contrast-enhanced three types brain tumors: meningioma (708 slices), glioma (1426 pituitary tumor (930 slices). experimental results demonstrate state-of-the-art performance, achieving SSIM 0.99, accuracy 99.4% diversity level 5, PSNR 34.6 dB. Our approach has potential high-fidelity augmentations reliable AI-driven clinical decision support systems.

Language: Английский

---

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

---