Hao-Feng Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Hao-Feng Liu has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Chia-Hui Liu
Department of Electronic Engineering, National Formosa University, Huwei Township 632301, Taiwan
1 shared publication - Hsuan-Yu Chen
Department of Radiology, Tri-Service General Hospital, National Defense Medical University, Taipei 114, Taiwan
1 shared publication - Shao-Wei Feng
Department of Neurological Surgery, Tri-Service General Hospital, National Defense Medical University, Taipei 114, Taiwan
1 shared publication - Zhen-Yu Wu
Department of Maritime Information and Technology, National Kaohsiung University of Science and Technology, Kaohsiung City, Taiwan
1 shared publication
Latest Publications
- Open Access
- 7 August 2025
- Published by MDPI AG in Engineering Proceedings
- Vol. 103 (1) , 8
Applying Deep Learning Techniques in Accurate Brain Tumor Detection and Classification
Magnetic resonance imaging (MRI), with its high resolution and radiation-free characteristics, has become a crucial tool for brain tumor diagnosis. We classified brain tumors into non-tumors, glioma, meningioma, and pituitary tumors by integrating public image datasets with preprocessing and data augmentation techniques and employing four deep learning models, such as a convolutional neural network (CNN), visual geometry group network 19 (VGGNet 9), residual network 101 version 2 (ResNet101V2), and efficient network version 2 b2 (EfficientNetV2B2). VGGNet19 and CNNs excelled in accuracy and stability, while EfficientNetV2B2 was efficient yet required refinement for specific categories, and ResNet101V2 benefited from further optimization. Deep learning significantly enhances diagnostic efficiency and accuracy, assisting clinical decision-making and improving patient survival rates.
- 7 August 2025
- Published by MDPI AG in Engineering Proceedings
- Vol. 103 (1) , 8
Open AccessApplying Deep Learning Techniques in Accurate Brain Tumor Detection and Classification
Magnetic resonance imaging (MRI), with its high resolution and radiation-free characteristics, has become a crucial tool for brain tumor diagnosis. We classified brain tumors into non-tumors, glioma, meningioma, and pituitary tumors by integrating public image datasets with preprocessing and data augmentation techniques and employing four deep learning models, such as a convolutional neural network (CNN), visual geometry group network 19 (VGGNet 9), residual network 101 version 2 (ResNet101V2), and efficient network version 2 b2 (EfficientNetV2B2). VGGNet19 and CNNs excelled in accuracy and stability, while EfficientNetV2B2 was efficient yet required refinement for specific categories, and ResNet101V2 benefited from further optimization. Deep learning significantly enhances diagnostic efficiency and accuracy, assisting clinical decision-making and improving patient survival rates.