AIMC Topic: Brain Neoplasms

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PatchResNet: Multiple Patch Division-Based Deep Feature Fusion Framework for Brain Tumor Classification Using MRI Images.

Journal of digital imaging
Modern computer vision algorithms are based on convolutional neural networks (CNNs), and both end-to-end learning and transfer learning modes have been used with CNN for image classification. Thus, automated brain tumor classification models have bee...

A Lightweight Deep Learning Based Microwave Brain Image Network Model for Brain Tumor Classification Using Reconstructed Microwave Brain (RMB) Images.

Biosensors
Computerized brain tumor classification from the reconstructed microwave brain (RMB) images is important for the examination and observation of the development of brain disease. In this paper, an eight-layered lightweight classifier model called micr...

Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients.

Computers in biology and medicine
Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma. This assessment is, however, complex to perform and associated with high variability due to the high heterogeneity and co...

MRI brain tumor segmentation using residual Spatial Pyramid Pooling-powered 3D U-Net.

Frontiers in public health
Brain tumor diagnosis has been a lengthy process, and automation of a process such as brain tumor segmentation speeds up the timeline. U-Nets have been a commonly used solution for semantic segmentation, and it uses a downsampling-upsampling approach...

MRI-based two-stage deep learning model for automatic detection and segmentation of brain metastases.

European radiology
OBJECTIVES: To develop and validate a two-stage deep learning model for automatic detection and segmentation of brain metastases (BMs) in MRI images.

Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Determination of H3 K27M mutation in diffuse midline glioma (DMG) is key for prognostic assessment and stratifying patient subgroups for clinical trials. MRI can noninvasively depict morphological and metabolic characteristics of H3 K27M ...

MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques.

BMC medical informatics and decision making
BACKGROUND: Detecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery. Computational intelligence-oriented techniques can help physicians identify and ...

AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study.

Acta neuropathologica communications
Automatic segmentation of rodent brain tumor on magnetic resonance imaging (MRI) may facilitate biomedical research. The current study aims to prove the feasibility for automatic segmentation by artificial intelligence (AI), and practicability of AI-...

Hybrid Optimization Algorithm Enabled Deep Learning Approach Brain Tumor Segmentation and Classification Using MRI.

Journal of digital imaging
The unnatural and uncontrolled increase of brain cells is called brain tumors, leading to human health danger. Magnetic resonance imaging (MRI) is widely applied for classifying and detecting brain tumors, due to its better resolution. In general, me...