AIMC Topic: Neural Networks, Computer

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Automatic diagnosis of neurological diseases using MEG signals with a deep neural network.

Scientific reports
The application of deep learning to neuroimaging big data will help develop computer-aided diagnosis of neurological diseases. Pattern recognition using deep learning can extract features of neuroimaging signals unique to various neurological disease...

A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis.

Computational and mathematical methods in medicine
This study reviews the technique of convolutional neural network (CNN) applied in a specific field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on how to use CNN for related tasks. MBCD is a long-standing problem, ...

Real-time gastric polyp detection using convolutional neural networks.

PloS one
Computer-aided polyp detection in gastric gastroscopy has been the subject of research over the past few decades. However, despite significant advances, automatic polyp detection in real time is still an unsolved problem. In this paper, we report on ...

PSO optimized 1-D CNN-SVM architecture for real-time detection and classification applications.

Computers in biology and medicine
In this paper, we propose a novel Particle Swarm Optimized (PSO) One-Dimensional Convolutional Neural Network with Support Vector Machine (1-D CNN-SVM) architecture for real-time detection and classification of diseases. The performance of the propos...

Patient-attentive sequential strategy for perimetry-based visual field acquisition.

Medical image analysis
Perimetry is a non-invasive clinical psychometric examination used for diagnosing ophthalmic and neurological conditions. At its core, perimetry relies on a subject pressing a button whenever they see a visual stimulus within their field of view. Thi...

Automatic knee meniscus tear detection and orientation classification with Mask-RCNN.

Diagnostic and interventional imaging
PURPOSE: This work presents our contribution to a data challenge organized by the French Radiology Society during the Journées Francophones de Radiologie in October 2018. This challenge consisted in classifying MR images of the knee with respect to t...

Evolution of Deep Convolutional Neural Networks Using Cartesian Genetic Programming.

Evolutionary computation
The convolutional neural network (CNN), one of the deep learning models, has demonstrated outstanding performance in a variety of computer vision tasks. However, as the network architectures become deeper and more complex, designing CNN architectures...

TeTrIS: Template Transformer Networks for Image Segmentation With Shape Priors.

IEEE transactions on medical imaging
In this paper, we introduce and compare different approaches for incorporating shape prior information into neural network-based image segmentation. Specifically, we introduce the concept of template transformer networks, where a shape template is de...

A predictive model for the evaluation of flavor attributes of raw and cooked beef based on sensor array analyses.

Food research international (Ottawa, Ont.)
There are currently no standardized objective measures to evaluate beef flavor attributes, especially the comparison between raw beef and cooked beef. Beef flavor attribute is one of the most significant parameters for consumers. This study described...

Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Deep learning techniques have been extensively used in computerized pulmonary nodule analysis in recent years. Many reported studies still utilized hybrid methods for diagnosis, in which convolutional neural networks (CNNs) are used only as one part ...