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Brain Neoplasms

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A deep image classification model based on prior feature knowledge embedding and application in medical diagnosis.

Scientific reports
Aiming at the problem of image classification with insignificant morphological structural features, strong target correlation, and low signal-to-noise ratio, combined with prior feature knowledge embedding, a deep learning method based on ResNet and ...

Deep learning automatic semantic segmentation of glioblastoma multiforme regions on multimodal magnetic resonance images.

International journal of computer assisted radiology and surgery
OBJECTIVES: In patients having naïve glioblastoma multiforme (GBM), this study aims to assess the efficacy of Deep Learning algorithms in automating the segmentation of brain magnetic resonance (MR) images to accurately determine 3D masks for 4 disti...

Breaking new ground: can artificial intelligence and machine learning transform papillary glioneuronal tumor diagnosis?

Neurosurgical review
Papillary glioneuronal tumors (PGNTs), classified as Grade I by the WHO in 2016, present diagnostic challenges due to their rarity and potential for malignancy. Xiaodan Du et al.'s recent study of 36 confirmed PGNT cases provides critical insights in...

Identification of Multiclass Pediatric Low-Grade Neuroepithelial Tumor Molecular Subtype with ADC MR Imaging and Machine Learning.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Molecular biomarker identification increasingly influences the treatment planning of pediatric low-grade neuroepithelial tumors (PLGNTs). We aimed to develop and validate a radiomics-based ADC signature predictive of the molec...

A comprehensive survey on the use of deep learning techniques in glioblastoma.

Artificial intelligence in medicine
Glioblastoma, characterized as a grade 4 astrocytoma, stands out as the most aggressive brain tumor, often leading to dire outcomes. The challenge of treating glioblastoma is exacerbated by the convergence of genetic mutations and disruptions in gene...

Multimodal Brain Tumor Classification Using Convolutional Tumnet Architecture.

Behavioural neurology
The most common and aggressive tumor is brain malignancy, which has a short life span in the fourth grade of the disease. As a result, the medical plan may be a crucial step toward improving the well-being of a patient. Both diagnosis and therapy are...

DTDO: Driving Training Development Optimization enabled deep learning approach for brain tumour classification using MRI.

Network (Bristol, England)
A brain tumour is an abnormal mass of tissue. Brain tumours vary in size, from tiny to large. Moreover, they display variations in location, shape, and size, which add complexity to their detection. The accurate delineation of tumour regions poses a ...

An effective ensemble learning approach for classification of glioma grades based on novel MRI features.

Scientific reports
The preoperative diagnosis of brain tumors is important for therapeutic planning as it contributes to the tumors' prognosis. In the last few years, the development in the field of artificial intelligence and machine learning has contributed greatly t...

Automated glioblastoma patient classification using hypoxia levels measured through magnetic resonance images.

BMC neuroscience
INTRODUCTION: The challenge of treating Glioblastoma (GBM) tumors is due to various mechanisms that make the tumor resistant to radiation therapy. One of these mechanisms is hypoxia, and therefore, determining the level of hypoxia can improve treatme...

Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning.

Journal of neuro-oncology
PURPOSE: High-grade glioma (HGG) is the most common and deadly malignant glioma of the central nervous system. The current standard of care includes surgical resection of the tumor, which can lead to functional and cognitive deficits. The aim of this...