Cited by A Comparative Study of Random Forest and Gradient Boosting Machine Learning Algorithms for Tumor Classification in Biomedical Images

Enhancing multiclass brain tumor diagnosis using SVM and innovative feature extraction techniques DOI

Mustafa Basthikodi,

M. Chaithrashree,

B. M. Ahamed Shafeeq

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 29, 2024

Abstract In the field of medical imaging, accurately classifying brain tumors remains a significant challenge because visual similarities among different tumor types. This research addresses multiclass categorization by employing Support Vector Machine (SVM) as core classification algorithm and analyzing its performance in conjunction with feature extraction techniques such Histogram Oriented Gradients (HOG) Local Binary Pattern (LBP), well dimensionality reduction technique, Principal Component Analysis (PCA). The study utilizes dataset sourced from Kaggle, comprising MRI images classified into four classes, captured various anatomical planes. Initially, SVM model alone attained an accuracy(acc_val) 86.57% on unseen test data, establishing baseline for performance. To enhance this, PCA was incorporated reduction, which improved acc_val to 94.20%, demonstrating effectiveness reducing mitigating overfitting enhancing generalization. Further gains were realized applying techniques—HOG LBP—in SVM, resulting 95.95%. most substantial improvement observed when combining both HOG, LBP, PCA, achieving impressive 96.03%, along F1 score(F1_val) 96.00%, precision(prec_val) 96.02%, recall(rec_val) 96.03%. approach will not only improves but also efficacy computation, making it robust effective method prediction.

Language: Английский

Citations

Advancing brain tumor segmentation and grading through integration of FusionNet and IBCO-based ALCResNet DOI

Abbas Rehman,

Naijie Gu, Asma Aldrees

et al.

Image and Vision Computing, Journal Year: 2025, Volume and Issue: unknown, P. 105432 - 105432

Published: Jan. 1, 2025

Language: Английский

Citations

Empowering brain cancer diagnosis: harnessing artificial intelligence for advanced imaging insights DOI

Omar S. Al-Kadi,

Roa’a Al-Emaryeen,

Sara Al-Nahhas

et al.

Reviews in the Neurosciences, Journal Year: 2024, Volume and Issue: 35(4), P. 399 - 419

Published: Jan. 30, 2024

Abstract Artificial intelligence (AI) is increasingly being used in the medical field, specifically for brain cancer imaging. In this review, we explore how AI-powered imaging can impact diagnosis, prognosis, and treatment of cancer. We discuss various AI techniques, including deep learning causality learning, their relevance. Additionally, examine current applications that provide practical solutions detecting, classifying, segmenting, registering tumors. Although challenges such as data quality, availability, interpretability, transparency, ethics persist, emphasise enormous potential intelligent standardising procedures enhancing personalised treatment, leading to improved patient outcomes. Innovative have power revolutionise neuro-oncology by quality routine clinical practice.

Language: Английский

Citations

Empowering Healthcare with AI: Brain Tumor Detection Using MRI and Multiple Algorithms DOI

Micheal Olaolu Arowolo,

Williams Oluwatobiloba Ajayi,

Prisca O. Olawoye

et al.

Published: April 2, 2024

Language: Английский

Citations

Medical Image Classification Using Deep Learning for Brain Tumors Detection: An Overview DOI

Hiba A. Alahmad,

Ghaida A. Al–Suhail

Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 194 - 207

Published: Jan. 1, 2024

Language: Английский

Citations

Therapeutic Decision Making in Prevascular Mediastinal Tumors Using CT Radiomics and Clinical Features: Upfront Surgery or Pretreatment Needle Biopsy? DOI

Chao‐Chun Chang, Chia-Ying Lin, Yi‐Sheng Liu

et al.

Cancers, Journal Year: 2024, Volume and Issue: 16(4), P. 773 - 773

Published: Feb. 13, 2024

The study aimed to develop machine learning (ML) classification models for differentiating patients who needed direct surgery from core needle biopsy among with prevascular mediastinal tumor (PMT). Patients PMT received a contrast-enhanced computed tomography (CECT) scan and initial management between January 2010 December 2020 were included in this retrospective study. Fourteen ML algorithms used construct candidate via the voting ensemble approach, based on preoperative clinical data radiomic features extracted CECT. accuracy of diagnosis was 86.1%. first model built by randomly choosing seven set fourteen had 88.0% (95% CI = 85.8 90.3%). second combination five models, including NeuralNetFastAI, NeuralNetTorch, RandomForest Entropy, Gini, XGBoost, 90.4% 87.9 93.0%), which significantly outperformed (p < 0.05). Due superior performance, clinical–radiomic may be as decision support system facilitate selection PMT.

Language: Английский

Citations

Detection of Brain Tumor Types Based on FANET Segmentation and Hybrid Squeeze Excitation Network with KNN DOI

Anjali Hemant Tiple, Anandrao B. Kakade, Uday A. Patil

et al.

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 227 - 245

Published: Jan. 1, 2024

Language: Английский

Citations

A Comparative Study of Random Forest and Gradient Boosting Machine Learning Algorithms for Tumor Classification in Biomedical Images DOI

K. Radhika,

Gulshan Dhasmana,

Muntather Almusawi

et al.

Published: May 9, 2024

Language: Английский

Citations