AIMC Topic: Neural Networks, Computer

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Biologically motivated learning method for deep neural networks using hierarchical competitive learning.

Neural networks : the official journal of the International Neural Network Society
This study proposes a novel biologically motivated learning method for deep convolutional neural networks (CNNs). The combination of CNNs and backpropagation learning is the most powerful method in recent machine learning regimes. However, it require...

Detection of COVID-19 from Chest X-ray Images Using Deep Convolutional Neural Networks.

Sensors (Basel, Switzerland)
The COVID-19 global pandemic has wreaked havoc on every aspect of our lives. More specifically, healthcare systems were greatly stretched to their limits and beyond. Advances in artificial intelligence have enabled the implementation of sophisticated...

Online and Offline Diagnosis of Motor Power Cables Based on 1D CNN and Periodic Burst Signal Injection.

Sensors (Basel, Switzerland)
We introduce a new approach for online and offline soft fault diagnosis in motor power cables, utilizing periodic burst injection and nonintrusive capacitive coupling. We focus on diagnosing soft faults because local cable modifications or soft fault...

Hyperspectral imaging and artificial intelligence to detect oral malignancy - part 1 - automated tissue classification of oral muscle, fat and mucosa using a light-weight 6-layer deep neural network.

Head & face medicine
BACKGROUND: Hyperspectral imaging (HSI) is a promising non-contact approach to tissue diagnostics, generating large amounts of raw data for whose processing computer vision (i.e. deep learning) is particularly suitable. Aim of this proof of principle...

DMC-Fusion: Deep Multi-Cascade Fusion With Classifier-Based Feature Synthesis for Medical Multi-Modal Images.

IEEE journal of biomedical and health informatics
Multi-modal medical image fusion is a challenging yet important task for precision diagnosis and surgical planning in clinical practice. Although single feature fusion strategy such as Densefuse has achieved inspiring performance, it tends to be not ...

Ophthalmic Disease Detection via Deep Learning With a Novel Mixture Loss Function.

IEEE journal of biomedical and health informatics
With the popularization of computer-aided diagnosis (CAD) technologies, more and more deep learning methods are developed to facilitate the detection of ophthalmic diseases. In this article, the deep learning-based detections for some common eye dise...

rBPDL:Predicting RNA-Binding Proteins Using Deep Learning.

IEEE journal of biomedical and health informatics
RNA-binding protein (RBP) is a powerful and wide-ranging regulator that plays an important role in cell development, differentiation, metabolism, health and disease. The prediction of RBPs provides valuable guidance for biologists. Although experimen...

A Combinatorial Deep Learning Structure for Precise Depth of Anesthesia Estimation From EEG Signals.

IEEE journal of biomedical and health informatics
Electroencephalography (EEG) is commonly used to measure the depth of anesthesia (DOA) because EEG reflects surgical pain and state of the brain. However, precise and real-time estimation of DOA index for painful surgical operations is challenging du...

Limitations of Transformers on Clinical Text Classification.

IEEE journal of biomedical and health informatics
Bidirectional Encoder Representations from Transformers (BERT) and BERT-based approaches are the current state-of-the-art in many natural language processing (NLP) tasks; however, their application to document classification on long clinical texts is...

An Attention-Based Mechanism to Combine Images and Metadata in Deep Learning Models Applied to Skin Cancer Classification.

IEEE journal of biomedical and health informatics
Computer-aided skin cancer classification systems built with deep neural networks usually yield predictions based only on images of skin lesions. Despite presenting promising results, it is possible to achieve higher performance by taking into accoun...