AIMC Topic: Brain Neoplasms

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A Hybrid CNN-GLCM Classifier For Detection And Grade Classification Of Brain Tumor.

Brain imaging and behavior
A supervised CNN Deep net classifier is proposed for the detection, classification and diagnosis of meningioma brain tumor using deep learning approach. This proposed method includes preprocessing, classification, and segmentation of the primary occu...

Deep learning-based convolutional neural network for intramodality brain MRI synthesis.

Journal of applied clinical medical physics
PURPOSE: The existence of multicontrast magnetic resonance (MR) images increases the level of clinical information available for the diagnosis and treatment of brain cancer patients. However, acquiring the complete set of multicontrast MR images is n...

Deep-learning and radiomics ensemble classifier for false positive reduction in brain metastases segmentation.

Physics in medicine and biology
Stereotactic radiosurgery (SRS) is now the standard of care for brain metastases (BMs) patients. The SRS treatment planning process requires precise target delineation, which in clinical workflow for patients with multiple (>4) BMs (mBMs) could becom...

Segmenting pediatric optic pathway gliomas from MRI using deep learning.

Computers in biology and medicine
Optic pathway gliomas are low-grade neoplastic lesions that account for approximately 3-5% of brain tumors in children. Assessing tumor burden from magnetic resonance imaging (MRI) plays a central role in its efficient management, yet it is a challen...

A Deep Learning Framework for Segmenting Brain Tumors Using MRI and Synthetically Generated CT Images.

Sensors (Basel, Switzerland)
Multi-modal three-dimensional (3-D) image segmentation is used in many medical applications, such as disease diagnosis, treatment planning, and image-guided surgery. Although multi-modal images provide information that no single image modality alone ...

Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques.

Journal of healthcare engineering
Radiology is a broad subject that needs more knowledge and understanding of medical science to identify tumors accurately. The need for a tumor detection program, thus, overcomes the lack of qualified radiologists. Using magnetic resonance imaging, b...

A pilot study of machine-learning based automated planning for primary brain tumours.

Radiation oncology (London, England)
PURPOSE: High-quality radiotherapy (RT) planning for children and young adults with primary brain tumours is essential to minimize the risk of late treatment effects. The feasibility of using automated machine-learning (ML) to aid RT planning in this...

Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model.

Sensors (Basel, Switzerland)
With the advancement in technology, machine learning can be applied to diagnose the mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for the early diagnosis of brain tum...

Pushing the boundaries of accuracy and reliability during stereotactic procedures: A prospective study on 526 biopsies comparing the frameless robotic and Image-Guided Surgery systems.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
INTRODUCTION: A 12-year long, prospective, single center study was conducted, comparing two frameless systems for brain biopsies: ROSA robotic-assisted stereotaxy and BrainLab Varioguide image-guided stereotaxy (Image Guided Surgery, IGS).

An Improved Brain MRI Classification Methodology Based on Statistical Features and Machine Learning Algorithms.

Computational and mathematical methods in medicine
In this paper, we have proposed a novel methodology based on statistical features and different machine learning algorithms. The proposed model can be divided into three main stages, namely, preprocessing, feature extraction, and classification. In t...