Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review
Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …
the health and well-being of millions of people worldwide. Structural and functional …
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
Gliomas are the most common primary brain malignancies, with different degrees of
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …
MRI brain tumor medical images analysis using deep learning techniques: a systematic review
The substantial progress of medical imaging technology in the last decade makes it
challenging for medical experts and radiologists to analyze and classify. Medical images …
challenging for medical experts and radiologists to analyze and classify. Medical images …
[HTML][HTML] 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 …
Attention-guided version of 2D UNet for automatic brain tumor segmentation
Gliomas are the most common and aggressive among brain tumors, which cause a short life
expectancy in their highest grade. Therefore, treatment assessment is a key stage to …
expectancy in their highest grade. Therefore, treatment assessment is a key stage to …
[HTML][HTML] Overall survival prediction in glioblastoma with radiomic features using machine learning
Glioblastoma is a WHO grade IV brain tumor, which leads to poor overall survival (OS) of
patients. For precise surgical and treatment planning, OS prediction of glioblastoma (GBM) …
patients. For precise surgical and treatment planning, OS prediction of glioblastoma (GBM) …
Brain tumor segmentation using cascaded deep convolutional neural network
Gliomas are the most common and threatening brain tumors with little to no survival rate.
Accurate detection of such tumors is crucial for survival of the subject. Naturally, tumors have …
Accurate detection of such tumors is crucial for survival of the subject. Naturally, tumors have …
Towards computationally efficient and realtime distracted driver detection with mobilevgg network
According to the World Health Organization (WHO) report, the number of road traffic deaths
have been continuously increasing since last few years though the rate of deaths relative to …
have been continuously increasing since last few years though the rate of deaths relative to …
Brain tumor segmentation and survival prediction
RR Agravat, MS Raval - International MICCAI Brainlesion Workshop, 2019 - Springer
The paper demonstrates the use of the fully convolutional neural network for glioma
segmentation on the BraTS 2019 dataset. Three-layers deep encoder-decoder architecture …
segmentation on the BraTS 2019 dataset. Three-layers deep encoder-decoder architecture …
Liver lesion changes analysis in longitudinal CECT scans by simultaneous deep learning voxel classification with SimU-Net
The identification and quantification of liver lesions changes in longitudinal contrast
enhanced CT (CECT) scans is required to evaluate disease status and to determine …
enhanced CT (CECT) scans is required to evaluate disease status and to determine …