scholarly journals Automatic late blight lesion recognition and severity quantification based on field imagery of diverse potato genotypes by deep learning

2020 ◽  
Author(s):  
Junfeng Gao ◽  
Jesper Cairo Westergaard ◽  
Ea Høegh Riis Sundmark ◽  
Merethe Bagge ◽  
Erland Liljeroth ◽  
...  
Keyword(s):  
Deep Learning ◽  
Late Blight ◽  
Disease Severity ◽  
Severe Disease ◽  
Learning Algorithms ◽  
Resistance Breeding ◽  
Potato Production ◽  
Majority Voting ◽  
Lesion Segmentation ◽  
Fast Evaluation

AbstractThe plant pathogen Phytophthora infestans causes the severe disease late blight in potato, which results in a huge loss for potato production. Automatic and accurate disease lesion segmentation enables fast evaluation of disease severity and assessment of disease progress for precision crop breeding. Deep learning has gained tremendous success in computer vision tasks for image classification, object detection and semantic segmentation. To test whether we could extract late blight lesions from unstructured field environments based on high-resolution visual field images and deep learning algorithms, we collected ~500 field RGB images in a set of diverse potato genotypes with different disease severity (0-70%), resulting in 2100 cropped images. 1600 of these cropped images were used as the dataset for training deep neural networks. Finally, the developed model was tested on the 250 cropped images. The results show that the intersection over union (IoU) values of background (leaf and soil) and disease lesion classes in the test dataset are 0.996 and 0.386, respectively. Furthermore, we established a linear relationship (R2 = 0.655) between manual visual scores of late blight and the number of lesions at the canopy level. We also learned that imbalance weights of lesion and background classes improved segmentation performance, and that fused masks based on the majority voting of the multiple masks enhanced the correlation with the visual scores. This study demonstrates the feasibility of using deep learning algorithms for disease lesion segmentation and severity evaluation based on proximal imagery for crop resistance breeding in field environments.

scholarly journals Assessing the Adequacy of the Simulation Model LATEBLIGHT Under Nicaraguan Conditions

Plant Disease ◽  
2011 ◽  
Vol 95 (7) ◽  
pp. 839-846 ◽  
Author(s):  
Jorge Ulises Blandón-Díaz ◽  
Gregory A. Forbes ◽  
Jorge L. Andrade-Piedra ◽  
Jonathan E. Yuen
Keyword(s):  
Late Blight ◽  
Simulation Model ◽  
Disease Severity ◽  
Field Experiments ◽  
Durable Resistance ◽  
Potato Production ◽  
Potato Cultivars ◽  
Disease Progress ◽  
High Disease Severity ◽  
Fungicide Control

In this study, the adequacy of the late blight simulation model LATEBLIGHT (version LB2004) was evaluated under Nicaraguan conditions. During 2007 to 2008, five field experiments were conducted in three potato-production regions in northern Nicaragua. Two susceptible (‘Cal White’ and ‘Granola’) and one resistant (‘Jacqueline Lee’) potato cultivars were evaluated without use of fungicides and with three application intervals (4, 7, and 14 days) of the fungicide chlorothalonil. The simulation model was considered adequate because it accurately predicted high disease severity in susceptible cultivars without fungicide protection, and demonstrated a decrease in the disease progress curves with additional fungicide applications, similar to that observed in the plots. The model also generally predicted inadequate fungicide control, even with a 4-day spray interval, which also occurred in the field. Lack of adequate fungicide protection would indicate the need for cultivars with higher levels of durable resistance, and that farmers should consider more effective fungicides applications (higher dosages or different chemistries) if susceptible cultivars are used.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

Rethinking the Dice Loss for Deep Learning Lesion Segmentation in Medical Images

2021 ◽  
Vol 26 (1) ◽  
pp. 93-102
Author(s):  
Yue Zhang ◽  
Shijie Liu ◽  
Chunlai Li ◽  
Jianyu Wang
Keyword(s):  
Deep Learning ◽  
Medical Images ◽  
Lesion Segmentation

scholarly journals Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran

2021 ◽  
Vol 35 ◽  
pp. 100825
Author(s):  
Mahdi Panahi ◽  
Khabat Khosravi ◽  
Sajjad Ahmad ◽  
Somayeh Panahi ◽  
Salim Heddam ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Infiltration Rate ◽  
Western Iran ◽  
Rate Prediction ◽  
Cumulative Infiltration

scholarly journals Serum C-Reactive Protein and Interleukin-6 Levels as Biomarkers for Disease Severity and Clinical Outcomes in Patients with Idiopathic Granulomatous Mastitis

2021 ◽  
Vol 10 (10) ◽  
pp. 2077
Author(s):  
Yi-Min Huang ◽  
Chiao Lo ◽  
Chiao-Feng Cheng ◽  
Cheng-Hsun Lu ◽  
Song-Chou Hsieh ◽  
...  
Keyword(s):  
Disease Severity ◽  
Severe Disease ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Serum Biomarkers ◽  
Healthy Controls ◽  
Granulomatous Mastitis ◽  
C Reactive Protein ◽  
Idiopathic Granulomatous Mastitis ◽  
Reactive Protein

Idiopathic granulomatous mastitis (IGM) is a rare inflammatory breast disease mimicking breast cancer. Limited research has been conducted on the application of serum biomarkers. This study aims to investigate the association of serum biomarkers with disease severity in patients with IGM. From November 2011 to March 2020, medical records of patients with IGM were reviewed. Serum cytokine levels were measured in patients and healthy controls between July 2018 and March 2020. A total of 41 patients with histologically proven IGM were found. Serum interleukin (IL)-6 level was significantly higher in patients with IGM (n = 11) than healthy controls (n = 7). Serum IL-6 and C-reactive protein (CRP) levels were significantly higher in patients with severe disease than mild and moderate disease. Serum IL-6 (Spearman’s ρ = 0.855; p < 0.001) and CRP (Spearman’s ρ = 0.838; p = 0.001) levels were associated with time to resolution. A higher serum CRP level was associated with a longer time to resolution (B = 0.322; p < 0.001) in multiple linear regression analysis. Serum IL-6 and CRP levels can be used as biomarkers for the evaluation of disease severity in IGM. IL-6 may play a crucial role in the immunopathology of IGM.

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

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