scholarly journals Non-invasive Classification of Sleep Stages with a Hydraulic Bed Sensor Using Deep Learning

2019 ◽  
pp. 73-82
Author(s):  
Rayan Gargees ◽  
James M. Keller ◽  
Mihail Popescu ◽  
Marjorie Skubic
Keyword(s):  
Deep Learning ◽  
Sleep Stages ◽  
Non Invasive

scholarly journals The Application of Deep Learning for the Classification of Internal Human Cardiac Anatomy

2018 ◽  
Author(s):  
Erik Gaasedelen ◽  
Alex Deakyne ◽  
Paul Iaizzo
Keyword(s):  
Deep Learning ◽  
Surgical Procedures ◽  
Electrical Conduction ◽  
Human Heart ◽  
Cardiac Anatomy ◽  
Non Invasive ◽  
Spatial Environment ◽  
The Creation

The applications of sensing and localization are becoming more sophisticated in many invasive and non-invasive surgical procedures and there is great interest to apply them to the human heart. Ideally, such tools could be indispensable for allowing physicians to spatially understand relative tissue morphologies and their associated electrical conduction. Yet today there remains a steep divide between the creation of spatial environment models and the contextual understandings of adjacent features. To begin to address this, we explore the problem of anatomical perception by applying deep learning to the identification of internal cardiac anatomy images.

Deep Learning for Automated Feature Discovery and Classification of Sleep Stages

2020 ◽  
Vol 17 (6) ◽  
pp. 1835-1845 ◽  
Author(s):  
Michael Sokolovsky ◽  
Francisco Guerrero ◽  
Sarun Paisarnsrisomsuk ◽  
Carolina Ruiz ◽  
Sergio A. Alvarez
Keyword(s):  
Deep Learning ◽  
Sleep Stages ◽  
Feature Discovery

scholarly journals Comparison of Object Detection Approaches Applied to Field Images of Papanicolaou Stained Cytology Slides

2021 ◽  
Author(s):  
André Victória Matias ◽  
Allan Cerentini ◽  
Luiz Antonio Buschetto Macarini ◽  
João Gustavo Atkinson Amorim ◽  
Felipe Perozzo Daltoé ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Deep Learning ◽  
Oral Cancer ◽  
Global Health ◽  
Object Detection ◽  
Learning Approach ◽  
Non Invasive ◽  
Global Health Issue ◽  
Invasive Method

Papanicolaou is an inexpensive and non-invasive method, generally applied to detect cervical cancer, that can also be useful to detect cancer on oral cavities. Although oral cancer is considered a global health issue with 350.000 people diagnosed over a year it can successfully be treated if diagnosed at early stages. The manual process of analyzing cells to detect abnormalities is time-consuming and subject to variations in perceptions from different professionals. To evaluate a possible solution to the automation of this process, in this paper we employ the object detection deep learning approach in the analysis of this type of image using 3 models: RetinaNet, Faster R-CNN, and Mask R-CNN. We trained and tested the models using images from 6 cytology slides (4 cancer cases and 2 healthy samples) and our results show that Mask R-CNN was the best model for localization and classification of nuclei with an IoU of 0.51 and recall of abnormal nuclei of 0.67.

scholarly journals Robust deep learning classification of adamantinomatous craniopharyngioma from limited preoperative radiographic images

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Eric W. Prince ◽  
Ros Whelan ◽  
David M. Mirsky ◽  
Nicholas Stence ◽  
Susan Staulcup ◽  
...  
Keyword(s):  
Deep Learning ◽  
Model Performance ◽  
Pediatric Brain Tumor ◽  
Training Data ◽  
Small Scale ◽  
Radiographic Images ◽  
Non Invasive ◽  
Magnetic Resonance Imaging Mri ◽  
Adamantinomatous Craniopharyngioma

Abstract Deep learning (DL) is a widely applied mathematical modeling technique. Classically, DL models utilize large volumes of training data, which are not available in many healthcare contexts. For patients with brain tumors, non-invasive diagnosis would represent a substantial clinical advance, potentially sparing patients from the risks associated with surgical intervention on the brain. Such an approach will depend upon highly accurate models built using the limited datasets that are available. Herein, we present a novel genetic algorithm (GA) that identifies optimal architecture parameters using feature embeddings from state-of-the-art image classification networks to identify the pediatric brain tumor, adamantinomatous craniopharyngioma (ACP). We optimized classification models for preoperative Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and combined CT and MRI datasets with demonstrated test accuracies of 85.3%, 83.3%, and 87.8%, respectively. Notably, our GA improved baseline model performance by up to 38%. This work advances DL and its applications within healthcare by identifying optimized networks in small-scale data contexts. The proposed system is easily implementable and scalable for non-invasive computer-aided diagnosis, even for uncommon diseases.

scholarly journals A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals

2019 ◽  
Vol 16 (4) ◽  
pp. 599 ◽  
Author(s):  
Ozal Yildirim ◽  
Ulas Baloglu ◽  
U Acharya
Keyword(s):  
Deep Learning ◽  
Neurological Diseases ◽  
Learning Model ◽  
Sleep Stages ◽  
One Dimensional ◽  
Proposed Model ◽  
Electroencephalogram Eeg ◽  
Deep Learning Model

Sleep disorder is a symptom of many neurological diseases that may significantly affect the quality of daily life. Traditional methods are time-consuming and involve the manual scoring of polysomnogram (PSG) signals obtained in a laboratory environment. However, the automated monitoring of sleep stages can help detect neurological disorders accurately as well. In this study, a flexible deep learning model is proposed using raw PSG signals. A one-dimensional convolutional neural network (1D-CNN) is developed using electroencephalogram (EEG) and electrooculogram (EOG) signals for the classification of sleep stages. The performance of the system is evaluated using two public databases (sleep-edf and sleep-edfx). The developed model yielded the highest accuracies of 98.06%, 94.64%, 92.36%, 91.22%, and 91.00% for two to six sleep classes, respectively, using the sleep-edf database. Further, the proposed model obtained the highest accuracies of 97.62%, 94.34%, 92.33%, 90.98%, and 89.54%, respectively for the same two to six sleep classes using the sleep-edfx dataset. The developed deep learning model is ready for clinical usage, and can be tested with big PSG data.

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