An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review
Background Artificial intelligence (AI) has become a prominent technique for medical
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …
Classification of weed using machine learning techniques: a review—challenges, current and future potential techniques
Weed detection and classification are considered one of the most vital tools in identifying
and recognizing plants in agricultural fields. Recently, machine learning techniques have …
and recognizing plants in agricultural fields. Recently, machine learning techniques have …
Attention Res-UNet with Guided Decoder for semantic segmentation of brain tumors
The automatic segmentation of brain tumors in Magnetic Resonance Imaging (MRI) plays a
major role in accurate diagnosis and treatment planning. The present study proposes a new …
major role in accurate diagnosis and treatment planning. The present study proposes a new …
[HTML][HTML] Swin transformer for fast MRI
Magnetic resonance imaging (MRI) is an important non-invasive clinical tool that can
produce high-resolution and reproducible images. However, a long scanning time is …
produce high-resolution and reproducible images. However, a long scanning time is …
Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3D FCN
J Sun, Y Peng, Y Guo, D Li - Neurocomputing, 2021 - Elsevier
Segmentation of multimodal brain tissues from 3D medical images is of great significance for
brain diagnosis. It is required to create an automated and accurate segmentation based on …
brain diagnosis. It is required to create an automated and accurate segmentation based on …
SAR-U-Net: Squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography
J Wang, P Lv, H Wang, C Shi - Computer Methods and Programs in …, 2021 - Elsevier
Background and objective Liver segmentation is an essential prerequisite for liver cancer
diagnosis and surgical planning. Traditionally, liver contour is delineated manually by …
diagnosis and surgical planning. Traditionally, liver contour is delineated manually by …
Brainseg-net: Brain tumor mr image segmentation via enhanced encoder–decoder network
Efficient segmentation of Magnetic Resonance (MR) brain tumor images is of the utmost
value for the diagnosis of tumor region. In recent years, advancement in the field of neural …
value for the diagnosis of tumor region. In recent years, advancement in the field of neural …
Hemorrhage semantic segmentation in fundus images for the diagnosis of diabetic retinopathy by using a convolutional neural network
Because retinal hemorrhage is one of the earliest symptoms of diabetic retinopathy, its
accurate identification is essential for early diagnosis. One of the major obstacles …
accurate identification is essential for early diagnosis. One of the major obstacles …
[HTML][HTML] Enhanced performance of Dark-Nets for brain tumor classification and segmentation using colormap-based superpixel techniques
S Ahuja, BK Panigrahi, TK Gandhi - Machine Learning with Applications, 2022 - Elsevier
The brain tumor is the deadliest disease in adults as it arises due to an abnormal mass of
cells that grows rapidly and it alters the proper functioning of the organs. In clinical practice …
cells that grows rapidly and it alters the proper functioning of the organs. In clinical practice …
FLWGAN: Federated Learning with Wasserstein Generative Adversarial Network for Brain Tumor Segmentation
Recently, the potential of deep learning in identifying complex patterns is gaining research
interest in medical applications specifically for brain tumor diagnosis. To segment tumors …
interest in medical applications specifically for brain tumor diagnosis. To segment tumors …