AIMC Topic: Brain Neoplasms

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Deep Learning for Smart Healthcare-A Survey on Brain Tumor Detection from Medical Imaging.

Sensors (Basel, Switzerland)
Advances in technology have been able to affect all aspects of human life. For example, the use of technology in medicine has made significant contributions to human society. In this article, we focus on technology assistance for one of the most comm...

Crossover based technique for data augmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Medical image classification problems are frequently constrained by the availability of datasets. "Data augmentation" has come as a data enhancement and data enrichment solution to the challenge of limited data. Traditionall...

Impaired phase synchronization of motor-evoked potentials reflects the degree of motor dysfunction in the lesioned human brain.

Human brain mapping
The functional corticospinal integrity (CSI) can be indexed by motor-evoked potentials (MEP) following transcranial magnetic stimulation of the motor cortex. Glial brain tumors in motor-eloquent areas are frequently disturbing CSI resulting in differ...

Enhanced Watershed Segmentation Algorithm-Based Modified ResNet50 Model for Brain Tumor Detection.

BioMed research international
This work delivers a novel technique to detect brain tumor with the help of enhanced watershed modeling integrated with a modified ResNet50 architecture. It also involves stochastic approaches to help in developing enhanced watershed modeling. Cancer...

Accelerating susceptibility-weighted imaging with deep learning by complex-valued convolutional neural network (ComplexNet): validation in clinical brain imaging.

European radiology
OBJECTIVES: Susceptibility-weighted imaging (SWI) is crucial for the characterization of intracranial hemorrhage and mineralization, but has the drawback of long acquisition times. We aimed to propose a deep learning model to accelerate SWI, and eval...

Application of Deep Learning Technology in Glioma.

Journal of healthcare engineering
A common and most basic brain tumor is glioma that is exceptionally dangerous to health of various patients. A glioma segmentation, which is primarily magnetic resonance imaging (MRI) oriented, is considered as one of common tools developed for docto...

Compound computer vision workflow for efficient and automated immunohistochemical analysis of whole slide images.

Journal of clinical pathology
AIMS: Immunohistochemistry (IHC) assessment of tissue is a central component of the modern pathology workflow, but quantification is challenged by subjective estimates by pathologists or manual steps in semi-automated digital tools. This study integr...

Brain Tumor Imaging: Applications of Artificial Intelligence.

Seminars in ultrasound, CT, and MR
Artificial intelligence has become a popular field of research with goals of integrating it into the clinical decision-making process. A growing number of predictive models are being employed utilizing machine learning that includes quantitative, com...

An efficient magnetic resonance image data quality screening dashboard.

Journal of applied clinical medical physics
PURPOSE: Complex data processing and curation for artificial intelligence applications rely on high-quality data sets for training and analysis. Manually reviewing images and their associated annotations is a very laborious task and existing quality ...

Investigating the Role of Image Fusion in Brain Tumor Classification Models Based on Machine Learning Algorithm for Personalized Medicine.

Computational and mathematical methods in medicine
Image fusion can be performed on images either in spatial domain or frequency domain methods. Frequency domain methods will be most preferred because these methods can improve the quality of edges in an image. In image fusion, the resultant fused ima...