AIMC Topic: Diagnosis, Computer-Assisted

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Round Randomized Learning Vector Quantization for Brain Tumor Imaging.

Computational and mathematical methods in medicine
Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ) is among...

Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features.

Schizophrenia research
Recently, an increasing number of researchers have endeavored to develop practical tools for diagnosing patients with schizophrenia using machine learning techniques applied to EEG biomarkers. Although a number of studies showed that source-level EEG...

Adaptive Dimensionality Reduction with Semi-Supervision (AdDReSS): Classifying Multi-Attribute Biomedical Data.

PloS one
Medical diagnostics is often a multi-attribute problem, necessitating sophisticated tools for analyzing high-dimensional biomedical data. Mining this data often results in two crucial bottlenecks: 1) high dimensionality of features used to represent ...

Support vector machines for automated snoring detection: proof-of-concept.

Sleep & breathing = Schlaf & Atmung
BACKGROUND: Snoring has been shown to be associated with adverse physical and mental health, independent of the effects of sleep disordered breathing. Despite increasing evidence for the risks of snoring, few studies on sleep and health include objec...

Multiclass imbalance learning: Improving classification of pediatric brain tumors from magnetic resonance spectroscopy.

Magnetic resonance in medicine
PURPOSE: Classification of pediatric brain tumors from H-magnetic resonance spectroscopy (MRS) can aid diagnosis and management of brain tumors. However, varied incidence of the different tumor types leads to imbalanced class sizes and introduces di...

Classification Preictal and Interictal Stages via Integrating Interchannel and Time-Domain Analysis of EEG Features.

Clinical EEG and neuroscience
The life quality of patients with refractory epilepsy is extremely affected by abrupt and unpredictable seizures. A reliable method for predicting seizures is important in the management of refractory epilepsy. A critical factor in seizure prediction...

On the development of conjunctival hyperemia computer-assisted diagnosis tools: Influence of feature selection and class imbalance in automatic gradings.

Artificial intelligence in medicine
OBJECTIVE: The sudden increase of blood flow in the bulbar conjunctiva, known as hyperemia, is associated to a red hue of variable intensity. Experts measure hyperemia using levels in a grading scale, a procedure that is subjective, non-repeatable an...

SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal.

IEEE transactions on bio-medical engineering
OBJECTIVE: This paper describes a data-analytic modeling approach for the prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics o...

Machine learning approaches in medical image analysis: From detection to diagnosis.

Medical image analysis
Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging...

Computer-Aided Diagnosis of Micro-Malignant Melanoma Lesions Applying Support Vector Machines.

BioMed research international
Background. One of the fatal disorders causing death is malignant melanoma, the deadliest form of skin cancer. The aim of the modern dermatology is the early detection of skin cancer, which usually results in reducing the mortality rate and less exte...