Semi-Supervised and Class-Imbalanced Open Set Medical Image Recognition DOI Creative Commons

Yiqian Xu,

Ruofan Wang, Rui-Wei Zhao

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 122852 - 122877

Published: Jan. 1, 2024

Language: Английский

Brain Tumor Segmentation Using Ensemble CNN-Transfer Learning Models: DeepLabV3plus and ResNet50 Approach DOI
Shoffan Saifullah, Rafał Dreżewski

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 340 - 354

Published: Jan. 1, 2024

Language: Английский

Citations

6
Automatic Brain Tumor Segmentation Using Convolutional Neural Networks: U-Net Framework with PSO-Tuned Hyperparameters DOI
Shoffan Saifullah, Rafał Dreżewski

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 333 - 351

Published: Jan. 1, 2024

Language: Английский

Citations

5
ViT-CB: Integrating hybrid Vision Transformer and CatBoost to enhanced brain tumor detection with SHAP DOI
Radius Tanone, Li-Hua Li, Shoffan Saifullah

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 100, P. 107027 - 107027

Published: Oct. 24, 2024

Language: Английский

Citations

4
CNN-Based Image Segmentation Approach in Brain Tumor Classification: A Review DOI Creative Commons
Nurul Huda, Ku Ruhana Ku‐Mahamud

Published: Feb. 17, 2025

This study explores the application of Convolutional Neural Networks (CNNs) for brain tumor segmentation, leveraging their ability to automatically extract hierarchical features from medical images. CNN architectures like U-Net, V-Net, and ResNet have shown significant promise in classification, offering high precision detecting boundaries classifying types. Various benchmark datasets, such as BraTS, TCIA, Harvard, Kaggle, provide annotated MRI images evaluate these models. Performance metrics including Dice Similarity Coefficient (DSC), Intersection over Union, accuracy are employed assess models' effectiveness. The results demonstrate that CNN-based models, particularly perform exceptionally well, with DSC scores exceeding 0.90 most cases. However, challenges data imbalance, need large computational demands persist. Despite limitations, CNNs, when combined advanced techniques transfer learning augmentation, offer robust solutions showing real-time clinical deployment. Further advancements necessary address generalization issues enhance model efficiency, ensuring broader applicability settings.

Language: Английский

Citations

0
Advanced brain tumor segmentation using DeepLabV3Plus with Xception encoder on a multi-class MR image dataset DOI
Shoffan Saifullah, Rafał Dreżewski, Anton Yudhana

et al.

Multimedia Tools and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 21, 2025

Language: Английский

Citations

0
Wavelet Guided Visual State Space Model and Patch Resampling Enhanced U-shaped Structure for Skin Lesion Segmentation DOI Creative Commons

Shuwan Feng,

Xiaowei Chen, Shengzhi Li

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 181521 - 181532

Published: Jan. 1, 2024

Language: Английский

Citations

1
Semi-Supervised and Class-Imbalanced Open Set Medical Image Recognition DOI Creative Commons

Yiqian Xu,

Ruofan Wang, Rui-Wei Zhao

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 122852 - 122877

Published: Jan. 1, 2024

Language: Английский

Citations

0