AI Medical Compendium Journal:
Medical hypotheses

Showing 1 to 10 of 20 articles

A preliminary evaluation of still face images by deep learning: A potential screening test for childhood developmental disabilities.

Medical hypotheses
Most developmental disorders are defined by their clinical symptoms and many disorders share common features. The main objective of this research is to evaluate still facial images as a potential screening test for childhood developmental disabilitie...

Brain tumor classification using modified local binary patterns (LBP) feature extraction methods.

Medical hypotheses
Automatic classification of brain tumor types is very important for accelerating the treatment process, planning and increasing the patient's survival rate. Today, MR images are used to determine the type of brain tumor. Manual diagnosis of brain tum...

Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture.

Medical hypotheses
Brain tumor is one of the dangerous and deadly cancer types seen in adults and children. Early and accurate diagnosis of brain tumor is important for the treatment process. It is an important step for specialists to detect the brain tumor using compu...

Diagnosis and classification of cancer using hybrid model based on ReliefF and convolutional neural network.

Medical hypotheses
Machine learning and deep learning methods aims to discover patterns out of datasets such as, microarray data and medical data. In recent years, the importance of producing microarray data from tissue and cell samples and analyzing these microarray d...

BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model.

Medical hypotheses
A brain tumor is a mass that grows unevenly in the brain and directly affects human life. This mass occurs spontaneously because of the tissues surrounding the brain or the skull. Surgical methods are generally preferred for the treatment of the brai...

A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method.

Medical hypotheses
Electroencephalography (EEG) signals have been widely used to diagnose brain diseases for instance epilepsy, Parkinson's Disease (PD), Multiple Skleroz (MS), and many machine learning methods have been proposed to develop automated disease diagnosis ...

Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware.

Medical hypotheses
Automatic decision support systems have gained importance in health sector in recent years. In parallel with recent developments in the fields of artificial intelligence and image processing, embedded systems are also used in decision support systems...

Automated Parkinson's disease recognition based on statistical pooling method using acoustic features.

Medical hypotheses
Parkinson's disease is one of the mostly seen neurological disease. It affects to nervous system and hinders people's vital activities. The majority of Parkinson's patients lose their ability to speak, write and balance. Many machine learning methods...

White blood cells detection and classification based on regional convolutional neural networks.

Medical hypotheses
White blood cells (WBC) are important parts of our immune system and they protect our body against infections by eliminating viruses, bacteria, parasites and fungi. There are five types of WBC. These are called Lymphocytes, Monocytes, Eosinophils, Ba...

Incorporating feature selection methods into a machine learning-based neonatal seizure diagnosis.

Medical hypotheses
The present study developed a feature selection (FS)-based decision support system using the electroencephalography (EEG) signals recorded from neonates with and without seizures. The study employed 10 different FS algorithms to reduce the classifica...