AIMC Journal:
IEEE journal of biomedical and health informatics

Showing 911 to 920 of 1118 articles

Efficient Brain Tumor Segmentation With Multiscale Two-Pathway-Group Conventional Neural Networks.

IEEE journal of biomedical and health informatics
Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious, and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, are crucial for the diagnosis, treatment planning, ...

A Hybrid Feature Selection Method Based on Binary State Transition Algorithm and ReliefF.

IEEE journal of biomedical and health informatics
Feature selection problems often appear in the application of data mining, which have been difficult to handle due to the NP-hard property of these problems. In this study, a simple but efficient hybrid feature selection method is proposed based on b...

A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation.

IEEE journal of biomedical and health informatics
Automatic retinal vessel segmentation is a fundamental step in the diagnosis of eye-related diseases, in which both thick vessels and thin vessels are important features for symptom detection. All existing deep learning models attempt to segment both...

Automatic Age Estimation and Majority Age Classification From Multi-Factorial MRI Data.

IEEE journal of biomedical and health informatics
Age estimation from radiologic data is an important topic both in clinical medicine as well as in forensic applications, where it is used to assess unknown chronological age or to discriminate minors from adults. In this paper, we propose an automati...

A Projection Neural Network for Identifying Copy Number Variants.

IEEE journal of biomedical and health informatics
The identification of copy number variations (CNVs) helps the diagnosis of many diseases. One major hurdle in the path of CNVs discovery is that the boundaries of normal and aberrant regions cannot be distinguished from the raw data, since various ty...

Towards End-to-End ECG Classification With Raw Signal Extraction and Deep Neural Networks.

IEEE journal of biomedical and health informatics
This paper proposes deep learning methods with signal alignment that facilitate the end-to-end classification of raw electrocardiogram (ECG) signals into heartbeat types, i.e., normal beat or different types of arrhythmias. Time-domain sample points ...

A Continuously Updated, Computationally Efficient Stress Recognition Framework Using Electroencephalogram (EEG) by Applying Online Multitask Learning Algorithms (OMTL).

IEEE journal of biomedical and health informatics
Recognizing the factors that cause stress is a crucial step toward early detection of stressors. In this regard, several studies make an effort to recognize individuals' stress using an Electroencephalogram (EEG). However, current EEG-based stress re...

Classifier Personalization for Activity Recognition Using Wrist Accelerometers.

IEEE journal of biomedical and health informatics
Intersubject variability in accelerometer-based activity recognition may significantly affect classification accuracy, limiting a reliable extension of methods to new users. In this paper, we propose an approach for personalizing classification rules...

Weakly Supervised Learning of Metric Aggregations for Deformable Image Registration.

IEEE journal of biomedical and health informatics
Deformable registration has been one of the pillars of biomedical image computing. Conventional approaches refer to the definition of a similarity criterion that, once endowed with a deformation model and a smoothness constraint, determines the optim...

Multimodal Medical Image Fusion Based on Fuzzy Discrimination With Structural Patch Decomposition.

IEEE journal of biomedical and health informatics
Multimodal medical image fusion, emerging as a hot topic, aims to fuse images with complementary multi-source information. In this paper, we propose a novel multimodal medical image fusion method based on structural patch decomposition (SPD) and fuzz...