scholarly journals Identification of public submitted tick images: A neural network approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260622
Author(s):  
Lennart Justen ◽  
Duncan Carlsmith ◽  
Susan M. Paskewitz ◽  
Lyric C. Bartholomay ◽  
Gebbiena M. Bron
Keyword(s):  
Public Health ◽  
Neural Network ◽  
Deep Learning ◽  
Ixodes Scapularis ◽  
Dermacentor Variabilis ◽  
Amblyomma Americanum ◽  
Identification Accuracy ◽  
Small Object ◽  
Neural Network Approach ◽  
Public Health Agencies

Ticks and tick-borne diseases represent a growing public health threat in North America and Europe. The number of ticks, their geographical distribution, and the incidence of tick-borne diseases, like Lyme disease, are all on the rise. Accurate, real-time tick-image identification through a smartphone app or similar platform could help mitigate this threat by informing users of the risks associated with encountered ticks and by providing researchers and public health agencies with additional data on tick activity and geographic range. Here we outline the requirements for such a system, present a model that meets those requirements, and discuss remaining challenges and frontiers in automated tick identification. We compiled a user-generated dataset of more than 12,000 images of the three most common tick species found on humans in the U.S.: Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis. We used image augmentation to further increase the size of our dataset to more than 90,000 images. Here we report the development and validation of a convolutional neural network which we call “TickIDNet,” that scores an 87.8% identification accuracy across all three species, outperforming the accuracy of identifications done by a member of the general public or healthcare professionals. However, the model fails to match the performance of experts with formal entomological training. We find that image quality, particularly the size of the tick in the image (measured in pixels), plays a significant role in the network’s ability to correctly identify an image: images where the tick is small are less likely to be correctly identified because of the small object detection problem in deep learning. TickIDNet’s performance can be increased by using confidence thresholds to introduce an “unsure” class and building image submission pipelines that encourage better quality photos. Our findings suggest that deep learning represents a promising frontier for tick identification that should be further explored and deployed as part of the toolkit for addressing the public health consequences of tick-borne diseases.

scholarly journals Identification of public submitted tick images: a neural network approach

2021 ◽  
Author(s):  
Lennart Justen ◽  
Duncan Carlsmith ◽  
Susan M Paskewitz ◽  
Lyric C. Bartholomay ◽  
Gebbiena M. Bron
Keyword(s):  
Public Health ◽  
Neural Network ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Ixodes Scapularis ◽  
Dermacentor Variabilis ◽  
Amblyomma Americanum ◽  
Identification Accuracy ◽  
Small Object ◽  
Neural Network Approach

Ticks and tick-borne diseases represent a growing public health threat in North America and Europe. The number of ticks, their geographical distribution, and the incidence of tick-borne diseases, like Lyme disease, are all on the rise. Accurate, real-time tick-image identification through a smartphone app or similar platform could help mitigate this threat by informing users of the risks associated and by providing researchers and public health agencies with better data on tick activity and geographic range. We report the development and validation of a convolutional neural network, a type of deep learning algorithm, trained on a dataset of more than 12,000 user-generated tick images. The model, which we call 'TickIDNet', is trained to identify the three most common tick species found on humans in the U.S.: Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis. At baseline, TickIDNet scores an 87.8% identification accuracy across all three species, outperforming the accuracy of identifications done by a member of the general public or healthcare professionals. However, the model fails to match the performance of experts with formal entomological training. We find that image quality, particularly the size of the tick in the image (measured in pixels), plays a significant role in the network's ability to correctly identify an image: images where the tick is small are less likely to be correctly identified because of the small object detection problem in deep learning. TickIDNet's performance can be increased by using confidence thresholds to introduce an 'unsure' class and building image submission pipelines that encourage better quality photos. Our findings suggest that deep learning represents a promising frontier for tick identification that should be further explored and deployed as part of the toolkit for addressing the public health consequences of tick-borne diseases.

Crowdsourced Photographs as an Effective Method for Large-Scale Passive Tick Surveillance

2020 ◽  
Vol 57 (6) ◽  
pp. 1955-1963 ◽  
Author(s):  
Heather L Kopsco ◽  
Guang Xu ◽  
Chu-Yuan Luo ◽  
Stephen M Rich ◽  
Thomas N Mather
Keyword(s):  
Large Scale ◽  
Linear Models ◽  
Ixodes Scapularis ◽  
Strong Support ◽  
Dermacentor Variabilis ◽  
Amblyomma Americanum ◽  
Identification Accuracy ◽  
Antibiotic Administration ◽  
Treatment And Prevention ◽  
Assessment And Monitoring

Abstract As tick vector ranges expand and the number of tickborne disease cases rise, physicians, veterinarians, and the public are faced with diagnostic, treatment, and prevention challenges. Traditional methods of active surveillance (e.g., flagging) can be time-consuming, spatially limited, and costly, while passive surveillance can broadly monitor tick distributions and infection rates. However, laboratory testing can require service fees in addition to mailing and processing time, which can put a tick-bite victim outside the window of potential prophylactic options or under unnecessary antibiotic administration. We performed a retrospective analysis of a national photograph-based crowdsourced tick surveillance system to determine the accuracy of identifying ticks by photograph when compared to those same ticks identified by microscopy and molecular methods at a tick testing laboratory. Ticks identified by photograph were correct to species with an overall accuracy of 96.7% (CI: 0.9522, 0.9781; P < 0.001), while identification accuracy for Ixodes scapularis Say (Ixodida: Ixodidae), Amblyomma americanum Linnaeus (Ixodida: Ixodidae), and Dermacentor variabilis Say (Ixodida: Ixodidae), three ticks of medical importance, was 98.2% (Cohen’s kappa [κ] = 0.9575; 95% CI: 0.9698, 0.9897), 98.8% (κ = 0.9466, 95% CI: 0.9776, 0.9941), and 98.8% (κ = 0.9515, 95% CI: 0.9776, 0.9941), respectively. Fitted generalized linear models revealed that tick species and stage were the most significant predictive factors that contributed to correct photograph-based tick identifications. Neither engorgement, season, nor location of submission affected identification ability. These results provide strong support for the utility of photograph-based tick surveillance as a tool for risk assessment and monitoring among commonly encountered ticks of medical concern.

Detection and Classification of Brain Tumors from MRI Images Using a Deep Convolutional Neural Network Approach

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Deep Convolutional Neural Network ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Cancer Disease

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Timely disease detection and treatment plans lead to the increased life expectancy of patients. Automated detection and classification of brain tumor are a more challenging process which is based on the clinician’s knowledge and experience. For this fact, one of the most practical and important techniques is to use deep learning. Recent progress in the fields of deep learning has helped the clinician’s in medical imaging for medical diagnosis of brain tumor. In this paper, we present a comparison of Deep Convolutional Neural Network models for automatically binary classification query MRI images dataset with the goal of taking precision tools to health professionals based on fined recent versions of DenseNet, Xception, NASNet-A, and VGGNet. The experiments were conducted using an MRI open dataset of 3,762 images. Other performance measures used in the study are the area under precision, recall, and specificity.

scholarly journals An Efficient Method for Detection of DDoS Attacks on the Web Using Deep Learning Algorithms

2021 ◽  
Vol 10 (4) ◽  
pp. 2821-2829
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Ddos Attacks ◽  
Problem Statement ◽  
Neural Network Approach ◽  
Learning Techniques ◽  
Attack Data ◽  
Deep Learning Neural Network

Recently, DDoS attacks is the most significant threat in network security. Both industry and academia are currently debating how to detect and protect against DDoS attacks. Many studies are provided to detect these types of attacks. Deep learning techniques are the most suitable and efficient algorithm for categorizing normal and attack data. Hence, a deep neural network approach is proposed in this study to mitigate DDoS attacks effectively. We used a deep learning neural network to identify and classify traffic as benign or one of four different DDoS attacks. We will concentrate on four different DDoS types: Slowloris, Slowhttptest, DDoS Hulk, and GoldenEye. The rest of the paper is organized as follow: Firstly, we introduce the work, Section 2 defines the related works, Section 3 presents the problem statement, Section 4 describes the proposed methodology, Section 5 illustrate the results of the proposed methodology and shows how the proposed methodology outperforms state-of-the-art work and finally Section VI concludes the paper.

scholarly journals Modeling the Distribution of Medically Important Tick Species in Florida

Insects ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 190 ◽  
Author(s):  
William H. Kessler ◽  
Claudia Ganser ◽  
Gregory E. Glass
Keyword(s):  
Habitat Suitability ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ixodes Scapularis ◽  
Dermacentor Variabilis ◽  
Amblyomma Americanum ◽  
Suitable Habitat ◽  
Public Health Importance ◽  
Raising Awareness ◽  
Great Public Health

The lone star (Amblyomma americanum), black-legged (Ixodes scapularis) and American dog ticks (Dermacentor variabilis) are species of great public health importance as they are competent vectors of several notable pathogens. While the regional distributions of these species are well characterized, more localized distribution estimates are sparse. We used records of field collected ticks and an ensemble modeling approach to predict habitat suitability for each of these species in Florida. Environmental variables capturing climatic extremes were common contributors to habitat suitability. Most frequently, annual precipitation (Bio12), mean temperature of the driest quarter (Bio9), minimum temperature of the coldest month (Bio6), and mean Normalized Difference Vegetation Index (NDVI) were included in the final models for each species. Agreement between the modeling algorithms used in this study was high and indicated the distribution of suitable habitat for all three species was reduced at lower latitudes. These findings are important for raising awareness of the potential for tick-borne pathogens in Florida.

Low Abundance of Three Tick Species in the Piedmont of North Carolina

2020 ◽  
Author(s):  
Madeline P Seagle ◽  
Maximilian R Vierling ◽  
Ryan J Almeida ◽  
D Jacob Clary ◽  
Will Hidell ◽  
...  
Keyword(s):  
North Carolina ◽  
Ixodes Scapularis ◽  
Dermacentor Variabilis ◽  
Amblyomma Americanum ◽  
Host Factors ◽  
Southeast United States ◽  
The North ◽  
Mecklenburg County ◽  
North Carolina Piedmont ◽  
Multiple Species

Abstract Multiple species of ticks, including Ixodes scapularis (Say, Ixodida:Ixodidae), Amblyomma americanum (L., Ixodida:Ixodidae), and Dermacentor variabilis (Say, Ixodida:Ixodidae), occur in high and increasing abundance in both the northeast and southeast United States. North Carolina is at the nexus of spread of these species, with high occurrence and abundance of I. scapularis to the north and A. americanum to the south. Despite this, there are few records of these species in the Piedmont of North Carolina, including the greater Charlotte metropolitan area. Here, we update the known occurrence and abundance of these species in the North Carolina Piedmont. We surveyed for ticks using cloth drags, CO2 traps, and leaf litter samples at a total of 79 sites within five locations: Mecklenburg County, South Mountains State Park, Stone Mountain State Park, Duke Forest, and Morrow Mountain State Park, all in North Carolina, during the late spring, summer, and fall seasons of 2019. From these surveys, we had only 20 tick captures, illuminating the surprisingly low abundance of ticks in this region of North Carolina. Our results indicate the possibility of underlying habitat and host factors limiting tick distribution and abundance in the North Carolina Piedmont.

scholarly journals Enhancing PMW Satellite Precipitation Estimation: Detecting Convective Class

2019 ◽  
Vol 36 (12) ◽  
pp. 2349-2363 ◽  
Author(s):  
Veljko Petković ◽  
Marko Orescanin ◽  
Pierre Kirstetter ◽  
Christian Kummerow ◽  
Ralph Ferraro
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Large Scale ◽  
Model Development ◽  
Precipitation Rate ◽  
Neural Network Approach ◽  
Satellite Precipitation ◽  
Precipitation Estimation ◽  
Microwave Imager ◽  
Deep Learning Neural Network

AbstractA decades-long effort in observing precipitation from space has led to continuous improvements of satellite-derived passive microwave (PMW) large-scale precipitation products. However, due to a limited ability to relate observed radiometric signatures to precipitation type (convective and stratiform) and associated precipitation rate variability, PMW retrievals are prone to large systematic errors at instantaneous scales. The present study explores the use of deep learning approach in extracting the information content from PMW observation vectors to help identify precipitation types. A deep learning neural network model (DNN) is developed to retrieve the convective type in precipitating systems from PMW observations. A 12-month period of Global Precipitation Measurement mission Microwave Imager (GMI) observations is used as a dataset for model development and verification. The proposed DNN model is shown to accurately predict precipitation types for 85% of total precipitation volume. The model reduces precipitation rate bias associated with convective and stratiform precipitation in the GPM operational algorithm by a factor of 2 while preserving the correlation with reference precipitation rates, and is insensitive to surface type variability. Based on comparisons against currently used convective schemes, it is concluded that the neural network approach has the potential to address regime-specific PMW satellite precipitation biases affecting GPM operations.

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