AIMC Journal:
Medical & biological engineering & computing

Showing 251 to 260 of 330 articles

Superpixel-based deep convolutional neural networks and active contour model for automatic prostate segmentation on 3D MRI scans.

Medical & biological engineering & computing
Automatic and reliable prostate segmentation is an essential prerequisite for assisting the diagnosis and treatment, such as guiding biopsy procedure and radiation therapy. Nonetheless, automatic segmentation is challenging due to the lack of clear p...

Convolutional neural network for detection and classification of seizures in clinical data.

Medical & biological engineering & computing
Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usual...

EEG classification across sessions and across subjects through transfer learning in motor imagery-based brain-machine interface system.

Medical & biological engineering & computing
Transfer learning enables the adaption of models to handle mismatches of distributions across sessions or across subjects. In this paper, we proposed a new transfer learning algorithm to classify motor imagery EEG data. By analyzing the power spectru...

Multi-view deep learning for rigid gas permeable lens base curve fitting based on Pentacam images.

Medical & biological engineering & computing
Many studies in the rigid gas permeable (RGP) lens fitting field have focused on providing the best fit for patients with irregular astigmatism, a challenging issue. Despite the ease and accuracy of fitting in the current fitting methods, no studies ...

A machine learning model for predicting risk of hospital readmission within 30 days of discharge: validated with LACE index and patient at risk of hospital readmission (PARR) model.

Medical & biological engineering & computing
The objective of this study was to design and develop a predictive model for 30-day risk of hospital readmission using machine learning techniques. The proposed predictive model was then validated with the two most commonly used risk of readmission m...

Classifying changes in LN-18 glial cell morphology: a supervised machine learning approach to analyzing cell microscopy data via FIJI and WEKA.

Medical & biological engineering & computing
In cell-based research, the process of visually monitoring cells generates large image datasets that need to be evaluated for quantifiable information in order to track the effectiveness of treatments in vitro. With the traditional, end-point assay-b...

Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training.

Medical & biological engineering & computing
High-quality annotations for medical images are always costly and scarce. Many applications of deep learning in the field of medical image analysis face the problem of insufficient annotated data. In this paper, we present a semi-supervised learning ...

Stable gene selection by self-representation method in fuzzy sample classification.

Medical & biological engineering & computing
In recent years, microarray technology and gene expression profiles have been widely used to detect, predict, or classify the samples of various diseases. The presence of large genes in these profiles and the small number of samples are known challen...

Low-contrast X-ray enhancement using a fuzzy gamma reasoning model.

Medical & biological engineering & computing
X-ray images play an important role in providing physicians with satisfactory information correlated to fractures and diseases; unfortunately, most of these images suffer from low contrast and poor quality. Thus, enhancement of the image will increas...

A fully automated hybrid human sperm detection and classification system based on mobile-net and the performance comparison with conventional methods.

Medical & biological engineering & computing
Sperm morphology, as an indicator of fertility, is a critical tool in semen analysis. In this study, a smartphone-based hybrid system that fully automates the sperm morphological analysis is introduced with the aim of eliminating unwanted human facto...