Medical image analysis using convolutional neural networks: a review
The science of solving clinical problems by analyzing images generated in clinical practice
is known as medical image analysis. The aim is to extract information in an affective and …
is known as medical image analysis. The aim is to extract information in an affective and …
A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification
Brain cancer classification is an important step that depends on the physician's knowledge
and experience. An automated tumor classification system is very essential to support …
and experience. An automated tumor classification system is very essential to support …
Deep learning based brain tumor classification and detection system
The brain cancer treatment process depends on the physician's experience and knowledge.
For this reason, using an automated tumor detection system is extremely important to aid …
For this reason, using an automated tumor detection system is extremely important to aid …
Enhanced MR image classification using hybrid statistical and wavelets features
Classification of brain tumor is one of the most vital tasks within medical image processing.
Classification of images greatly depends on the features extracted from the image, and thus …
Classification of images greatly depends on the features extracted from the image, and thus …
Detection and classification of Encephalon tumor using extreme learning machine learning algorithm based on Deep Learning Method
The ordinary people cannot have the capability to detect and classify the brain tumor, 9 so
the radiologists or the clinical experts are the only person who can detect the encephalon …
the radiologists or the clinical experts are the only person who can detect the encephalon …
A deep learning approach for multi‐stage classification of brain tumor through magnetic resonance images
Brain tumor is the 10th major cause of death among humans. The detection of brain tumor is
a significant process in the medical field. Therefore, the objective of this research work is to …
a significant process in the medical field. Therefore, the objective of this research work is to …
A dual-branch hybrid dilated CNN model for the AI-assisted segmentation of meningiomas in MR images
X Ma, Y Zhao, Y Lu, P Li, X Li, N Mei, J Wang… - Computers in Biology …, 2022 - Elsevier
Background and objective: Treatment for meningiomas usually includes surgical removal,
radiation therapy, and chemotherapy. Accurate segmentation of tumors significantly …
radiation therapy, and chemotherapy. Accurate segmentation of tumors significantly …
Multiclass brain Glioma tumor classification using block-based 3D Wavelet features of MR images
With the advent of more powerful computing devices, system automation plays a pivotal role.
In the medical industry, automated image classification and segmentation is an important …
In the medical industry, automated image classification and segmentation is an important …
Di‐phase midway convolution and deconvolution network for brain tumor segmentation in MRI images
PL Chithra, G Dheepa - International Journal of Imaging …, 2020 - Wiley Online Library
A novel automatic image segmentation technique in magnetic resonance imaging (MRI)
based on di‐phase midway convolution and deconvolution network is proposed. It consists …
based on di‐phase midway convolution and deconvolution network is proposed. It consists …
Brief Review for Multi-Class Brain Tumor Diseases Schemes Using Machine Learning Techniques
Brain tumor diseases have had a considerable impact worldwide, affecting millions of
individuals of different age groups, including both children and adults above 20 years old …
individuals of different age groups, including both children and adults above 20 years old …