Clinical decision support framework for segmentation and classification of brain tumor MRIs using a U-Net and DCNN cascaded learning algorithm
Brain tumors (BTs) are an uncommon but fatal kind of cancer. Therefore, the development of
computer-aided diagnosis (CAD) systems for classifying brain tumors in magnetic …
computer-aided diagnosis (CAD) systems for classifying brain tumors in magnetic …
WD‐UNeXt: Weight loss function and dropout U‐Net with ConvNeXt for automatic segmentation of few shot brain gliomas
Z Yin, H Gao, J Gong, Y Wang - IET Image Processing, 2023 - Wiley Online Library
Accurate segmentation of brain gliomas (BG) is a crucial and challenging task for effective
treatment planning in BG therapy. This study presents the weight loss function and dropout …
treatment planning in BG therapy. This study presents the weight loss function and dropout …
G-Net: Implementing an enhanced brain tumor segmentation framework using semantic segmentation design
CS DS, C Clement J - Plos one, 2024 - journals.plos.org
A fundamental computer vision task called semantic segmentation has significant uses in
the understanding of medical pictures, including the segmentation of tumors in the brain …
the understanding of medical pictures, including the segmentation of tumors in the brain …
Detecting Routing Protocol Low Power and Lossy Network Attacks Using Machine Learning Techniques
The Internet of Things (loT) has been regarded as the most critical technology due to its
resource-constrained sensors transmitted via low-power wireless technologies beneath low …
resource-constrained sensors transmitted via low-power wireless technologies beneath low …
An Effective Deep Learning Approach for the Estimation of Proton Energy by Using Artificial Neural Network
The prediction of proton energy shows a key part in various scientific and technological
studies including particle physics, medical imaging, and radiation therapy. In the last years …
studies including particle physics, medical imaging, and radiation therapy. In the last years …
Lung Cancer Detection Using Machine Learning and Deep Learning Models
Technology and artificial intelligence play a significant role in improving healthcare and
enable tasks to be automated. In addition, the diseases can be better understood and …
enable tasks to be automated. In addition, the diseases can be better understood and …
A Birds Species Detection Utilizing an Effective Hybrid Model
SA Rafa, ZM Al-qfail, AA Nafea… - … Multi-Conference on …, 2024 - ieeexplore.ieee.org
The aim of this study is to improve the performance of detection Birds Species by proposed a
hybrid model using a combination of MobileNetV2 for feature extraction, an Autoencoder for …
hybrid model using a combination of MobileNetV2 for feature extraction, an Autoencoder for …
Assessing layer normalization with brats mri data in a convolution neural net
Abstract Deep learning-based Convolutional Neural Network (CNN) architectures are
commonly used in medical imaging. Medical imaging data is highly imbalanced. A deep …
commonly used in medical imaging. Medical imaging data is highly imbalanced. A deep …