Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models

Cancers, Journal Year: 2025, Volume and Issue: 17(1), P. 121 - 121

Published: Jan. 2, 2025

Background/Objectives: Brain tumor classification is a crucial task in medical diagnostics, as early and accurate detection can significantly improve patient outcomes. This study investigates the effectiveness of pre-trained deep learning models classifying brain MRI images into four categories: Glioma, Meningioma, Pituitary, No Tumor, aiming to enhance diagnostic process through automation. Methods: A publicly available Tumor dataset containing 7023 was used this research. The employs state-of-the-art models, including Xception, MobileNetV2, InceptionV3, ResNet50, VGG16, DenseNet121, which are fine-tuned using transfer learning, combination with advanced preprocessing data augmentation techniques. Transfer applied fine-tune optimize accuracy while minimizing computational requirements, ensuring efficiency real-world applications. Results: Among tested Xception emerged top performer, achieving weighted 98.73% F1 score 95.29%, demonstrating exceptional generalization capabilities. These proved particularly effective addressing class imbalances delivering consistent performance across various evaluation metrics, thus their suitability for clinical adoption. However, challenges persist improving recall Glioma Meningioma categories, black-box nature requires further attention interpretability trust settings. Conclusions: findings underscore transformative potential imaging, offering pathway toward more reliable, scalable, efficient tools. Future research will focus on expanding diversity, model explainability, validating settings support widespread adoption AI-driven systems healthcare ensure integration workflows.

Language: Английский

EO-LGBM-HAR: A novel meta-heuristic hybrid model for human activity recognition

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 189, P. 110004 - 110004

Published: March 17, 2025

Language: Английский

Fusion‐Brain‐Net: A Novel Deep Fusion Model for Brain Tumor Classification

Brain and Behavior, Journal Year: 2025, Volume and Issue: 15(5)

Published: May 1, 2025

ABSTRACT Problem Brain tumors are among the most prevalent and lethal diseases. Early diagnosis precise treatment crucial. However, manual classification of brain is a laborious complex task. Aim This study aimed to develop fusion model address certain limitations previous works, such as covering diverse image modalities in various datasets. Method We presented hybrid transfer learning model, Fusion‐Brain‐Net, at automatic tumor classification. The proposed method included four stages: preprocessing data augmentation, deep feature extractions, fine‐tuning, Integrating pre‐trained CNN models, VGG16, ResNet50, MobileNetV2, enhanced comprehensive extraction while mitigating overfitting issues, improving model's performance. Results was rigorously tested verified on public datasets: Br35H, Figshare, Nickparvar, Sartaj. It achieved remarkable accuracy rates 99.66%, 97.56%, 97.08%, 93.74%, respectively. Conclusion numerical results highlight that should be further investigated for potential use computer‐aided diagnoses improve clinical decision‐making.

Language: Английский