In-domain transfer learning strategy for tumor detection on brain MRI

DS Terzi, N Azginoglu - Diagnostics, 2023 - mdpi.com
Transfer learning has gained importance in areas where there is a labeled data shortage.
However, it is still controversial as to what extent natural image datasets as pre-training …

[HTML][HTML] Analysis of Hybrid Feature Optimization Techniques Based on the Classification Accuracy of Brain Tumor Regions Using Machine Learning and Further …

S Pal, RP Singh, A Kumar - Journal of Medical Physics, 2024 - journals.lww.com
Aim: The goal of this study was to get optimal brain tumor features from magnetic resonance
imaging (MRI) images and classify them based on the three groups of the tumor region …

[PDF][PDF] Batch Normalization Based Convolutional Neural Network for Segmentation and Classification of Brain Tumor MRI Images.

G Bompem, D Pandluri - International Journal of Intelligent Engineering & …, 2024 - inass.org
The uncontrolled growth of cells in human brain can lead to the formation of tumors, which
can occur in all age people. The tumor in brain can affect nerve cells, soft tissues and blood …

Auto-segmentation of Adult-Type Diffuse Gliomas: Comparison of Transfer Learning-Based Convolutional Neural Network Model vs. Radiologists

Q Wan, J Kim, C Lindsay, X Chen, J Li… - Journal of Imaging …, 2024 - Springer
Segmentation of glioma is crucial for quantitative brain tumor assessment, to guide
therapeutic research and clinical management, but very time-consuming. Fully automated …

Comparison of Tumor Segmentation Techniques from Medical Images

B Gururaj, PP Nair, L Harish… - … Conference on I …, 2023 - ieeexplore.ieee.org
A Brain Tumor (BT) is one of the leading causes of mortality worldwide. Because of this,
early diagnosis is crucial. BT localization and segmentation from magnetic resonance …