Automatic detection and counting of retina cell nuclei using deep learning

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

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Automatic detection of papilledema through fundus retinal images using deep learning

Microscopy Research and Technique ◽

10.1002/jemt.23865 ◽

2021 ◽

Author(s):

Tanzila Saba ◽

Shahzad Akbar ◽

Hoshang Kolivand ◽

Saeed Ali Bahaj

Keyword(s):

Deep Learning ◽

Automatic Detection ◽

Retinal Images

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Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R

Environmental Pollution ◽

10.1016/j.envpol.2021.116490 ◽

2021 ◽

Vol 273 ◽

pp. 116490

Author(s):

Odei Garcia-Garin ◽

Toni Monleón-Getino ◽

Pere López-Brosa ◽

Asunción Borrell ◽

Alex Aguilar ◽

...

Keyword(s):

Deep Learning ◽

Web Application ◽

Automatic Detection ◽

Aerial Images ◽

Learning Approach ◽

Detection And Quantification

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Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning

Journal of Digital Imaging ◽

10.1007/s10278-021-00477-8 ◽

2021 ◽

Author(s):

Ranjana Agrawal ◽

Sucheta Kulkarni ◽

Rahee Walambe ◽

Ketan Kotecha

Keyword(s):

Deep Learning ◽

Retinopathy Of Prematurity ◽

Automatic Detection

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Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models

Remote Sensing ◽

10.3390/rs13142822 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2822

Author(s):

Zhe Lin ◽

Wenxuan Guo

Keyword(s):

Deep Learning ◽

Cotton Plant ◽

Unmanned Aerial System ◽

Learning Models ◽

Training Images ◽

Testing Dataset ◽

Cotton Plants ◽

Detection And Counting ◽

Different Dimensions ◽

The Mean

An accurate stand count is a prerequisite to determining the emergence rate, assessing seedling vigor, and facilitating site-specific management for optimal crop production. Traditional manual counting methods in stand assessment are labor intensive and time consuming for large-scale breeding programs or production field operations. This study aimed to apply two deep learning models, the MobileNet and CenterNet, to detect and count cotton plants at the seedling stage with unmanned aerial system (UAS) images. These models were trained with two datasets containing 400 and 900 images with variations in plant size and soil background brightness. The performance of these models was assessed with two testing datasets of different dimensions, testing dataset 1 with 300 by 400 pixels and testing dataset 2 with 250 by 1200 pixels. The model validation results showed that the mean average precision (mAP) and average recall (AR) were 79% and 73% for the CenterNet model, and 86% and 72% for the MobileNet model with 900 training images. The accuracy of cotton plant detection and counting was higher with testing dataset 1 for both CenterNet and MobileNet models. The results showed that the CenterNet model had a better overall performance for cotton plant detection and counting with 900 training images. The results also indicated that more training images are required when applying object detection models on images with different dimensions from training datasets. The mean absolute percentage error (MAPE), coefficient of determination (R2), and the root mean squared error (RMSE) values of the cotton plant counting were 0.07%, 0.98 and 0.37, respectively, with testing dataset 1 for the CenterNet model with 900 training images. Both MobileNet and CenterNet models have the potential to accurately and timely detect and count cotton plants based on high-resolution UAS images at the seedling stage. This study provides valuable information for selecting the right deep learning tools and the appropriate number of training images for object detection projects in agricultural applications.

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Automatic Detection of Thyroid and Adrenal Incidentals Using Radiology Reports and Deep Learning

Journal of Surgical Research ◽

10.1016/j.jss.2021.03.060 ◽

2021 ◽

Vol 266 ◽

pp. 192-200

Author(s):

Stephen P. Canton ◽

Esmaeel Dadashzadeh ◽

Linwah Yip ◽

Raquel Forsythe ◽

Robert Handzel

Keyword(s):

Deep Learning ◽

Automatic Detection ◽

Radiology Reports

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A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone

Drones ◽

10.3390/drones5010006 ◽

2021 ◽

Vol 5 (1) ◽

pp. 6

Author(s):

Apostolos Papakonstantinou ◽

Marios Batsaris ◽

Spyros Spondylidis ◽

Konstantinos Topouzelis

Keyword(s):

Deep Learning ◽

Data Acquisition ◽

Citizen Science ◽

Coastal Zone ◽

Automatic Detection ◽

Monitoring Methods ◽

Monitoring Method ◽

Marine Litter ◽

Density Maps ◽

Learning Techniques

Marine litter (ML) accumulation in the coastal zone has been recognized as a major problem in our time, as it can dramatically affect the environment, marine ecosystems, and coastal communities. Existing monitoring methods fail to respond to the spatiotemporal changes and dynamics of ML concentrations. Recent works showed that unmanned aerial systems (UAS), along with computer vision methods, provide a feasible alternative for ML monitoring. In this context, we proposed a citizen science UAS data acquisition and annotation protocol combined with deep learning techniques for the automatic detection and mapping of ML concentrations in the coastal zone. Five convolutional neural networks (CNNs) were trained to classify UAS image tiles into two classes: (a) litter and (b) no litter. Testing the CCNs’ generalization ability to an unseen dataset, we found that the VVG19 CNN returned an overall accuracy of 77.6% and an f-score of 77.42%. ML density maps were created using the automated classification results. They were compared with those produced by a manual screening classification proving our approach’s geographical transferability to new and unknown beaches. Although ML recognition is still a challenging task, this study provides evidence about the feasibility of using a citizen science UAS-based monitoring method in combination with deep learning techniques for the quantification of the ML load in the coastal zone using density maps.

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