Developing deep learning models to automate rosewood tree species identification for CITES designation and implementation

Holzforschung ◽  
2020 ◽  
Vol 74 (12) ◽  
pp. 1123-1133 ◽  
Author(s):  
Tuo He ◽  
Yang Lu ◽  
Lichao Jiao ◽  
Yonggang Zhang ◽  
Xiaomei Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wood Species ◽  
Cost Effective ◽  
Timber Species ◽  
Learning Models ◽  
Field Screening ◽  
Anatomical Features ◽  
Wild Fauna ◽  
Parameter Configuration ◽  
Feature Visualization

AbstractThe implementation of Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) to combat illegal logging and associated trade necessitates accurate and efficient field screening of wood species. In this study, a total of 10,237 images of 15 Dalbergia and 11 Pterocarpus species were collected from the transverse surfaces of 417 wood specimens. Three deep learning models were then constructed, trained, and tested with these images to discriminate between timber species. The optimal parameters of the deep learning model were analyzed, and the representative wood anatomical features that were activated by the deep learning models were visualized. The results demonstrated that the overall accuracies of the 26-class, 15-class, and 11-class models were 99.3, 93.7, and 88.4%, respectively. It is suggested that at least 100 high-quality images per species with minimum patch sizes of 1000 × 1000 from more than 10 wood specimens were needed to train reliable and applicable deep learning models. The feature visualization indicated that the vessel groupings and axial parenchyma were the main wood anatomical features activated by the deep learning models. The combination of the state-of-the-art deep learning models, parameter configuration, and feature visualization provide a time- and cost-effective tool for the field screening of wood species to support effective CITES designation and implementation.

scholarly journals Deep Learning Models for Poorly Differentiated Colorectal Adenocarcinoma Classification in Whole Slide Images Using Transfer Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2074
Author(s):  
Masayuki Tsuneki ◽  
Fahdi Kanavati
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Characteristic Curve ◽  
Cost Effective ◽  
Endoscopic Biopsy ◽  
Histopathological Diagnosis ◽  
Average Risk ◽  
Learning Models ◽  
Poorly Differentiated ◽  
Whole Slide Images

Colorectal poorly differentiated adenocarcinoma (ADC) is known to have a poor prognosis as compared with well to moderately differentiated ADC. The frequency of poorly differentiated ADC is relatively low (usually less than 5% among colorectal carcinomas). Histopathological diagnosis based on endoscopic biopsy specimens is currently the most cost effective method to perform as part of colonoscopic screening in average risk patients, and it is an area that could benefit from AI-based tools to aid pathologists in their clinical workflows. In this study, we trained deep learning models to classify poorly differentiated colorectal ADC from Whole Slide Images (WSIs) using a simple transfer learning method. We evaluated the models on a combination of test sets obtained from five distinct sources, achieving receiver operating characteristic curve (ROC) area under the curves (AUCs) up to 0.95 on 1799 test cases.

scholarly journals Artificial intelligence–based detection of aortic stenosis from chest radiographs

2021 ◽  
Author(s):  
Daiju Ueda ◽  
Akira Yamamoto ◽  
Shoichi Ehara ◽  
Shinichi Iwata ◽  
Koji Abo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Aortic Stenosis ◽  
Predictive Value ◽  
Cost Effective ◽  
Chest Radiographs ◽  
Validation Dataset ◽  
Learning Models ◽  
Test Dataset ◽  
Sensitivity Specificity

Abstract Aims We aimed to develop models to detect aortic stenosis (AS) from chest radiographs—one of the most basic imaging tests—with artificial intelligence. Methods and Results We used 10433 retrospectively collected digital chest radiographs from 5638 patients to train, validate, and test three deep learning models. Chest radiographs were collected from patients who had also undergone echocardiography at a single institution between July 2016 and May 2019. These were labelled from the corresponding echocardiography assessments as AS-positive or AS-negative. The radiographs were separated on a patient basis into training (8327 images from 4512 patients, mean age 65 ± [SD] 15 years), validation (1041 images from 563 patients, mean age 65 ± 14 years), and test (1065 images from 563 patients, mean age 65 ± 14 years) datasets. The soft voting-based ensemble of the three developed models had the best overall performance for predicting AS with an AUC, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of 0.83 (95% CI 0.77–0.88), 0.78 (0.67–0.86), 0.71 (0.68–0.73), 0.71 (0.68–0.74), 0.18 (0.14–0.23), and 0.97 (0.96–0.98), respectively, in the validation dataset and 0.83 (0.78–0.88), 0.83 (0.74–0.90), 0.69 (0.66–0.72), 0.71 (0.68–0.73), 0.23 (0.19–0.28), and 0.97 (0.96–0.98), respectively, in the test dataset. Conclusion Deep learning models using chest radiographs have the potential to differentiate between radiographs of patients with and without AS. Lay summary We created AI models using deep learning to identify aortic stenosis from chest radiographs. Three AI models were developed and evaluated with 10433 retrospectively collected radiographs and labelled from echocardiography reports. The ensemble AI model could detect aortic stenosis in a test dataset with an AUC of 0.83 (95% CI 0.78–0.88). Since chest radiography is a cost effective and widely available imaging test, our model can provide an additive resource for the detection of aortic stenosis.

scholarly journals Deep learning models for poorly differentiated colorectal adenocarcinoma classification in whole slide images using transfer learning

2021 ◽  
Author(s):  
Masayuki Tsuneki ◽  
Fahdi Kanavati
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Cost Effective ◽  
Endoscopic Biopsy ◽  
Histopathological Diagnosis ◽  
Average Risk ◽  
Learning Models ◽  
Poorly Differentiated ◽  
Roc Area ◽  
Whole Slide Images

Colorectal poorly differentiated adenocarcinoma (ADC) is known to have a poor prognosis as compared with well to moderately differentiated ADC. The frequency of poorly differentiated ADC is relatively low (usually less than 5% among colorectal carcinomas). Histopathological diagnosis based on endoscopic biopsy specimens is currently the most cost effective method to perform as part of colonoscopic screening in average risk patients, and it is an area that could benefit from AI-based tools to aid pathologists in their clinical workflows. In this study, we trained deep learning models to classify poorly differentiated colorectal ADC from Whole Slide Images (WSIs) using a simply transfer learning method. We evaluated the models on a combination of test sets obtained from five distinct sources, achieving receiver operator curve (ROC) area under the curves (AUCs) in the range of 0.94-0.98.

scholarly journals Comparison of Classification Models Based on Deep learning on COVID-19 Chest X-Rays

2022 ◽  
Vol 2161 (1) ◽  
pp. 012078
Author(s):  
Pallavi R Mane ◽  
Rajat Shenoy ◽  
Ghanashyama Prabhu
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
State Of The Art ◽  
Cost Effective ◽  
X Rays ◽  
The Novel ◽  
Learning Models ◽  
X Ray ◽  
Chest X Ray ◽  
Fine Tune

Abstract COVID -19, is a deadly, dangerous and contagious disease caused by the novel corona virus. It is very important to detect COVID-19 infection accurately as quickly as possible to avoid the spreading. Deep learning methods can significantly improve the efficiency and accuracy of reading Chest X-Rays (CXRs). The existing Deep learning models with further fine tune provide cost effective, rapid, and better classification results. This paper tries to deploy well studied AI tools with modification on X-ray images to classify COVID 19. This research performs five experiments to classify COVID-19 CXRs from Normal and Viral Pneumonia CXRs using Convolutional Neural Networks (CNN). Four experiments were performed on state-of-the-art pre-trained models using transfer learning and one experiment was performed using a CNN designed from scratch. Dataset used for the experiments consists of chest X-Ray images from the Kaggle dataset and other publicly accessible sources. The data was split into three parts while 90% retained for training the models, 5% each was used in validation and testing of the constructed models. The four transfer learning models used were Inception, Xception, ResNet, and VGG19, that resulted in the test accuracies of 93.07%, 94.8%, 67.5%, and 91.1% respectively and our CNN model resulted in 94.6%.

Innovative Field Screening Tools for a Reliable and Cost-effective Site Characterization

2004 ◽  
Vol 12 (2) ◽  
pp. 115-125
Author(s):  
Katrin Batereau ◽  
Martin Müller ◽  
Norbert Klaas ◽  
Baldur Barczewski
Keyword(s):  
Cost Effective ◽  
Site Characterization ◽  
Screening Tools ◽  
Field Screening ◽  
Effective Site

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

2019 ◽  
Author(s):  
Mohammad Rezaei ◽  
Yanjun Li ◽  
Xiaolin Li ◽  
Chenglong Li
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Binding Affinity ◽  
Benchmark Dataset ◽  
Rational Drug Design ◽  
Learning Models ◽  
Structure Based Drug Design ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Rational Drug

<b>Introduction:</b> The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results.<br><b>Objectives:</b> The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters.<br><b>Methods:</b> The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models.<br><b>Results: </b>The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model.<br><b>Conclusions:</b> The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

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.

Deep Learning in Disease Diagnosis: Models and Datasets

2020 ◽  
Vol 15 ◽  
Author(s):  
Deeksha Saxena ◽  
Mohammed Haris Siddiqui ◽  
Rajnish Kumar
Keyword(s):  
Biological Sciences ◽  
Machine Learning ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Models ◽  
Data Types ◽  
Related Data ◽  
Abstract Level ◽  
Experimental Validations ◽  
Selection Of

Background: Deep learning (DL) is an Artificial neural network-driven framework with multiple levels of representation for which non-linear modules combined in such a way that the levels of representation can be enhanced from lower to a much abstract level. Though DL is used widely in almost every field, it has largely brought a breakthrough in biological sciences as it is used in disease diagnosis and clinical trials. DL can be clubbed with machine learning, but at times both are used individually as well. DL seems to be a better platform than machine learning as the former does not require an intermediate feature extraction and works well with larger datasets. DL is one of the most discussed fields among the scientists and researchers these days for diagnosing and solving various biological problems. However, deep learning models need some improvisation and experimental validations to be more productive. Objective: To review the available DL models and datasets that are used in disease diagnosis. Methods: Available DL models and their applications in disease diagnosis were reviewed discussed and tabulated. Types of datasets and some of the popular disease related data sources for DL were highlighted. Results: We have analyzed the frequently used DL methods, data types and discussed some of the recent deep learning models used for solving different biological problems. Conclusion: The review presents useful insights about DL methods, data types, selection of DL models for the disease diagnosis.

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