Add to ORKGA Feature Selection (FS) process with a simple Machine Learning method, namely the Single-Layer Perceptron (SLP), is shown to discriminate metastases from glioblastomas with high accuracy using single voxel H-MRS from an international, multi-centre database of brain tumors. The method has low computational cost and its results are intuitively interpretable
We evaluated the diagnostic performance and generalizability of traditional machine learning and dee...
Brain Gliomas is one among the biggest threat faced by many people around the globe. According to In...
Purpose: Glioblastoma Multiform (GBM) is one of the most common and deadly malignant brain tumors. S...
A Feature Selection process with Single-Layer Perceptrons is shown to provide optimum discrimination...
Machine learning has provided, over the last decades, tools for knowledge extraction in complex medi...
Purpose Differentiation of glioblastomas from metastases is clinical important, but may be difficult...
Hydrogen-1 magnetic resonance spectroscopy (1H-MRS) allows noninvasive in vivo quantification of met...
Glioma and brain metastasis can be difficult to distinguish on conventional magnetic resonance imagi...
Supervised Machine Learning (SML) an extremely powerful classifier wasapplied for diagnosing the var...
Magnetic resonance spectroscopy (MRS); Linear discriminant analysis (LDA); Support vector machine (S...
Treatment planning and prognosis in glioma treatment are based on the classification into low- and h...
Abstract — Hydrogen-1 magnetic resonance spectroscopy ( 1H-MRS) allows noninvasive in vivo quantific...
In neuro oncology, the accurate diagnostic identification and characterization of tumours is paramou...
In cancer diagnosis, classification of the different tumor types is of great importance. An accurate...
Background and purpose: Differentiating glioblastoma from solitary brain metastasis preoperatively u...
We evaluated the diagnostic performance and generalizability of traditional machine learning and dee...
Brain Gliomas is one among the biggest threat faced by many people around the globe. According to In...
Purpose: Glioblastoma Multiform (GBM) is one of the most common and deadly malignant brain tumors. S...
A Feature Selection process with Single-Layer Perceptrons is shown to provide optimum discrimination...
Machine learning has provided, over the last decades, tools for knowledge extraction in complex medi...
Purpose Differentiation of glioblastomas from metastases is clinical important, but may be difficult...
Hydrogen-1 magnetic resonance spectroscopy (1H-MRS) allows noninvasive in vivo quantification of met...
Glioma and brain metastasis can be difficult to distinguish on conventional magnetic resonance imagi...
Supervised Machine Learning (SML) an extremely powerful classifier wasapplied for diagnosing the var...
Magnetic resonance spectroscopy (MRS); Linear discriminant analysis (LDA); Support vector machine (S...
Treatment planning and prognosis in glioma treatment are based on the classification into low- and h...
Abstract — Hydrogen-1 magnetic resonance spectroscopy ( 1H-MRS) allows noninvasive in vivo quantific...
In neuro oncology, the accurate diagnostic identification and characterization of tumours is paramou...
In cancer diagnosis, classification of the different tumor types is of great importance. An accurate...
Background and purpose: Differentiating glioblastoma from solitary brain metastasis preoperatively u...
We evaluated the diagnostic performance and generalizability of traditional machine learning and dee...
Brain Gliomas is one among the biggest threat faced by many people around the globe. According to In...
Purpose: Glioblastoma Multiform (GBM) is one of the most common and deadly malignant brain tumors. S...