Enhanced Brain Tumor Segmentation and Size Estimation in MRI Samples using Hybrid Optimization DOI
Ayesha Agrawal, Vinod Maan

Data & Metadata, Journal Year: 2023, Volume and Issue: 2, P. 408 - 408

Published: Dec. 26, 2023

The area of medical imaging specialization, specifically in the context brain tumor segmentation, has long been challenged by inherent complexity and variability structures. Traditional segmentation methods often struggle to accurately differentiate between diverse types tissues within brain, such as white matter, grey cerebrospinal fluid, leading suboptimal results identification delineation. These limitations necessitate development more advanced precise techniques enhance diagnostic accuracy treatment planning. In response these challenges, proposed study introduces a novel approach that combines Grey Wolf Optimization Cuckoo Search Fuzzy C-Means (FCM) framework. integration GWO CS is designed leverage their respective strengths optimizing tissues. This hybrid was rigorously tested across multiple Magnetic Resonance Imaging (MRI) datasets, demonstrating significant enhancements over existing methods. observed 4,9 % improvement accuracy, 3,5 increase precision, 4,5 higher recall, 3,2 less delay, 2,5 better specificity segmentation. implications advancements are profound. By achieving precision method can substantially aid early diagnosis accurate staging tumors, eventually effective planning improved patient outcomes. Furthermore, FCM process sets new benchmark imaging, paving way for future investigation field

Language: Английский

SPBTGNS: Design of an Efficient Model for Survival Prediction in Brain Tumour Patients using Generative Adversarial Network with Neural Architectural Search Operations DOI Creative Commons

Ruqsar Zaitoon,

Sachi Nandan Mohanty, Deepthi Godavarthi

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 140847 - 140869

Published: Jan. 1, 2024

The landscape of medical imaging, particularly in brain tumor analysis and survival prediction, necessitates advancements due to the inherent complexities life-threatening nature tumors. Existing methodologies often struggle with precision efficiency, predominantly limitations handling diverse intricate image datasets. This research presents a novel approach that aims improve accuracy prediction patients tumours, leveraging Generative Adversarial Network (GAN) integrated Neural Architectural Search (NAS) operations. model employs Adaptive Computation Time (ACT) Transformer, method crucial for dynamically adjusting number transformer layers based on complexity input sets. feature is beneficial imaging adapting varying data samples. integration Squeeze-and-Excitation Networks (SENet) enables recalibrate features channel-wise, significantly enhancing sensitivity pivotal MRI images. Furthermore, application Google's AutoML Vision Edge offers efficient neural architecture hyperparameter optimization, specifically tuned Efficient Architecture (ENAS) utilized discover high-performance models lower computational demands, critical aspect where resource constraints are common different use cases. also incorporates customized loss functions, Weighted Cross-Entropy Loss, addressing class imbalance datasets by emphasizing rarer types. Spatial Dropout Batch Normalization as regularization techniques generalization reduce overfitting risks. model's efficacy was validated Br35H, Kaggle Brain Tumor Dataset, IEEE Data Port Dataset Databases, exhibiting notable improvement over existing methods: 5.9% better precision, 6.5% higher accuracy, 4.9% recall analysis. In analysis, demonstrated 8.5% 8.3% among other improvements. These enhancements underscore capability providing more accurate, efficient, reliable predictions patients, potentially revolutionizing diagnosis prognostication clinical settings.

Language: Английский

Citations

6
Defining quantitative rules for identifying influential researchers: Insights from mathematics domain DOI Creative Commons
Ghulam Mustafa, Abid Rauf, Ahmad Sami Al-Shamayleh

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(9), P. e30318 - e30318

Published: April 29, 2024

In the midst of a vast amount scientific literature, need for specific rules arise especially when it comes to deciding which impactful researchers should be nominated. These are based on measurable quantities that can easily applied researcher's quantitative data. Various search engines, like Google Scholar, Semantic Web Science etc. Are used recording metadata such as total publications, their citations, h-index However, community has not yet agreed upon single set criteria researcher meet in order secure spot list researchers. this study, we have provided comprehensive within field mathematics, derived from top five parameters belonging each category. Within categorical grouping, meticulously selected most pivotal parameters. This selection process was guided by an importance score, after assessing its influence model's performance classification data pertaining both awardees and non awardees. To perform experiment, focused mathematics dataset containing 525 individuals who received awards did receive awards. The were developed parameter category using Decision Tree Algorithm, achieved average accuracy 70 75 percent identifying domains. Moreover, highest-ranked successful elevating over 50 55 award recipients positions 100 ranked researchers' list. findings potential serve guidance individual researchers, aimed making esteemed distinguished scientists. Additionally, utilize these sift through roster subjective evaluation, facilitating recognition rewarding exceptional field.

Language: Английский

Citations

4
Advancements in deep learning techniques for brain tumor segmentation: A survey DOI Creative Commons

C. Umarani,

Shantappa G. Gollagi, Shridhar Allagi

et al.

Informatics in Medicine Unlocked, Journal Year: 2024, Volume and Issue: 50, P. 101576 - 101576

Published: Jan. 1, 2024

Citations

4
Unveiling Insights: AlexNet-Driven MRI Analysis for Precision Diagnosis of Knee Disorders DOI

K. B. K. S. Durga,

M. Shanmuga Sundari,

K. Akshaya

et al.

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 319 - 329

Published: Jan. 1, 2025

Language: Английский

Citations

0
A transfer learning based model for brain tumor detection using magnetic resonance imaging DOI
Saurabh Srivastava, Tasneem Ahmed, Rajeev Kumar

et al.

AIP conference proceedings, Journal Year: 2025, Volume and Issue: 3224, P. 020059 - 020059

Published: Jan. 1, 2025

Language: Английский

Citations

0
Brain Tumor Segmentation and Classification using MRI: Modified Segnet Model and Hybrid Deep Learning Architecture with Improved Texture Features DOI

Palleti Venkata Kusuma,

S. Chandra Mohan Reddy

Computational Biology and Chemistry, Journal Year: 2025, Volume and Issue: 117, P. 108381 - 108381

Published: Feb. 18, 2025

Language: Английский

Citations

0
MK-SMOTE and M-SMOTE: enhanced techniques for handling class imbalance problem DOI

Asifa Kanwal,

Nayyer Masood, Ghulam Mustafa

et al.

Iran Journal of Computer Science, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 24, 2025

Language: Английский

Citations

0
A Comprehensive Framework for Brain MRI Analysis: Classification, Segmentation, and Survival Prediction DOI

Bh V. S. Ramakrishnam Raju,

Mohan Satya Durga Jonnala,

Jaswanth Dasari

et al.

Smart innovation, systems and technologies, Journal Year: 2025, Volume and Issue: unknown, P. 593 - 607

Published: Jan. 1, 2025

Language: Английский

Citations

0
Optimized Multi-model Framework for Enhanced and Timely Brain Tumor Classification Leveraging DeepLoc, Machine Learning Architectures, and Ensemble Learning Technique DOI Open Access
Vineet Mehan

Journal of Advances in Information Technology, Journal Year: 2025, Volume and Issue: 16(4), P. 491 - 498

Published: Jan. 1, 2025

Language: Английский

Citations

0
Enhancing author assessment: an advanced modified recursive elimination technique (MRET) for ranking key parameters and conducting statistical analysis of top-ranked parameter DOI
Ghulam Mustafa, Abid Rauf, Muhammad Tanvir Afzal

et al.

International Journal of Data Science and Analytics, Journal Year: 2024, Volume and Issue: unknown

Published: April 22, 2024

Language: Английский

Citations

2