scholarly journals Comparative Study of Ensemble Models of Deep Convolutional  Neural Networks for Crop Pests Classification

2021 ◽  
Author(s):  
zhongbin su ◽  
jiaqi luo ◽  
yue wang ◽  
qingming kong ◽  
baisheng dai
Keyword(s):  
Performance Metrics ◽  
Insect Pest ◽  
Automatic Classification ◽  
Pest Species ◽  
Learning Models ◽  
Deep Convolutional Neural Networks ◽  
Crop Pests ◽  
Ensemble Models ◽  
Basic Learning

Abstract Pest infestations on wheat, corn, soybean, and other crops can cause substantial losses to their yield. Early diagnosis and automatic classification of various insect pest categories are of considerable importance for accurate and intelligent pest control. However, given the wide variety of crop pests and the high degree of resemblance between certain pest species, the automatic classification of pests can be very challenging. To improve the classification accuracy on publicly available D0 dataset with 40 classes, this paper compares studies on the use of ensemble models for crop pests classification. First, six basic learning models as Xception, InceptionV3, Vgg16, Vgg19, Resnet50, MobileNetV2 are trained on D0 dataset. Then, three models with the best classification performance are selected. Finally, the ensemble models, i.e, linear ensemble named SAEnsemble and nonlinear ensemble SBPEnsemble, are designed to combine the basic learning models for crop pests classification. The accuracies of SAEnsemble and SBPEnsemble improved by 0.85% and 1.49% respectively compared to basic learning model with the highest accuracy. Comparison of the two proposed ensemble models show that they have different performance under different condition. In terms of performance metrics, SBPEnsemble giving accuracy of classification at 96.18%, is more competitive than SAEnsemble.

Multi-level learning features for automatic classification of field crop pests

2018 ◽  
Vol 152 ◽  
pp. 233-241 ◽  
Author(s):  
Chengjun Xie ◽  
Rujing Wang ◽  
Jie Zhang ◽  
Peng Chen ◽  
Wei Dong ◽  
...  
Keyword(s):  
Automatic Classification ◽  
Field Crop ◽  
Crop Pests ◽  
Multi Level

scholarly journals Explainable AI to improve acceptance of convolutional neural networks for automatic classification of dopamine transporter SPECT in the diagnosis of clinically uncertain parkinsonian syndromes

2021 ◽  
Author(s):  
Mahmood Nazari ◽  
Andreas Kluge ◽  
Ivayla Apostolova ◽  
Susanne Klutmann ◽  
Sharok Kimiaei ◽  
...  
Keyword(s):  
Neural Networks ◽  
Dopamine Transporter ◽  
Convolutional Neural Networks ◽  
Automatic Classification ◽  
Clinical Workflow ◽  
Deep Convolutional Neural Networks ◽  
Parkinsonian Syndromes ◽  
Custom Made ◽  
Dat Spect

Abstract Purpose Deep convolutional neural networks (CNN) provide high accuracy for automatic classification of dopamine transporter (DAT) SPECT images. However, CNN are inherently black-box in nature lacking any kind of explanation for their decisions. This limits their acceptance for clinical use. This study tested layer-wise relevance propagation (LRP) to explain CNN-based classification of DAT-SPECT in patients with clinically uncertain parkinsonian syndromes. Methods The study retrospectively included 1296 clinical DAT-SPECT with visual binary interpretation as “normal” or “reduced” by two experienced readers as standard-of-truth. A custom-made CNN was trained with 1008 randomly selected DAT-SPECT. The remaining 288 DAT-SPECT were used to assess classification performance of the CNN and to test LRP for explanation of the CNN-based classification. Results Overall accuracy, sensitivity, and specificity of the CNN were 95.8%, 92.8%, and 98.7%, respectively. LRP provided relevance maps that were easy to interpret in each individual DAT-SPECT. In particular, the putamen in the hemisphere most affected by nigrostriatal degeneration was the most relevant brain region for CNN-based classification in all reduced DAT-SPECT. Some misclassified DAT-SPECT showed an “inconsistent” relevance map more typical for the true class label. Conclusion LRP is useful to provide explanation of CNN-based decisions in individual DAT-SPECT and, therefore, can be recommended to support CNN-based classification of DAT-SPECT in clinical routine. Total computation time of 3 s is compatible with busy clinical workflow. The utility of “inconsistent” relevance maps to identify misclassified cases requires further investigation.

scholarly journals Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Xavier P. Burgos-Artizzu ◽  
David Coronado-Gutiérrez ◽  
Brenda Valenzuela-Alcaraz ◽  
Elisenda Bonet-Carne ◽  
Elisenda Eixarch ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automatic Classification ◽  
Deep Convolutional Neural Networks ◽  
Fetal Ultrasound

scholarly journals COVID-19 Identification using Machine Learning Classifiers with GLCM Features of Chest X-ray Images

2021 ◽  
Vol 18 (23) ◽  
pp. 46
Author(s):  
Sudeep D. Thepade ◽  
Hrishikesh Jha
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Automated System ◽  
Learning Models ◽  
X Ray ◽  
Chest X Ray ◽  
Rapid Pace ◽  
Occurrence Matrix ◽  
Machine Learning Models

COVID-19 is an ongoing pandemic, and is also known by the name coronavirus. It was originally discovered in Wuhan, China, in December, 2019. Since then, it has been increasing rapidly worldwide. Since it has been increasing at such a rapid pace, testing equipment has limited availability. Also, this disease spreads very quickly, so it is better if it is detected earlier, in order so that it can be stopped from spreading. Therefore, the importance of early detection has increased; however, because of the shortage of testing sets, it is a necessity to develop an automated system that can detect whether the COVID-19 disease is present in a person or not as early as possible. Therefore, in this work, to extract features from X-ray images of the chest, we have made use of the Gray Level Co-occurrence Matrix (GLCM). After extracting these features for the classification of the images, we used different machine learning models, and an ensemble of machine learning models, to classify X-ray images of the chest as COVID-19, Normal, Pneumonia-bac, or Pneumonia-vir. Considering the average of performance metrics, the ensemble of Random Forest-MLP gave the best result among the variations.

AI-enabled Algorithm for Automatic Classification of Sleep Disorders Based on Single-lead Electrocardiogram (Preprint)

2021 ◽  
Author(s):  
Erdenebayar Urtnasan ◽  
Eun Yeon Joo ◽  
Kyuhee Lee
Keyword(s):  
Sleep Disorders ◽  
Sleep Disorder ◽  
Screening Method ◽  
Rem Sleep Behavior Disorder ◽  
Automatic Classification ◽  
Control Group ◽  
Ecg Signal ◽  
Deep Convolutional Neural Networks ◽  
Sleep Behavior

BACKGROUND Healthy sleep is an essential and important physiological process for every individual to live a healthy life. Many sleep disorders are both destroying the quality and decreasing the duration of sleep. Thus, a convenient and accurate detection or classification method is important for screening and identifying sleep disorders. OBJECTIVE In this study, we proposed an AI-enabled algorithm for automatic classification of sleep disorders based on a single-lead electrocardiogram (ECG). AI-enabled algorithm—named a sleep-disorder network (SDN) was designed for automatic classification of four major sleep-disorders namely insomnia (INS), periodic leg movement (PLM), REM sleep Behavior Disorder (RBD), and nocturnal frontal-lobe epilepsy (NFE). METHODS The SDN was constructed using deep convolutional neural networks that can extract and analyze the complex and cyclic rhythm of sleep disorders that affect ECG patterns. The SDN consists of 5-layers 1-D convolutional layer and is optimized via dropout and batch normalization. The single-lead ECG signal was extracted from the 35 subjects with the control (CNT) and the four sleep-disorder groups (7 subjects of each group) in the CAP Sleep Database. The ECG signal was pre-processed, segmented at 30-s intervals, and divided into the training, validation, and test sets consisting of 74,135, 18,534, and 23,168 segments, respectively. The constructed SDN was trained and evaluated using the CAP Sleep Database, which contains not only data on sleep disorders, but also data of the control group. RESULTS The proposed SDN algorithm for the automatic classification of sleep disorders based on a single-lead ECG showed very high performances. We achieved F1-scores of 99.0%, 97.0%, 97.0%, 95.0%, and 98.0% for the CNT, INS, PLM, RBD, and NFE groups, respectively. CONCLUSIONS We proposed an AI-enabled method for the automatic classification of sleep disorders based on a single-lead ECG signal. In addition, it represents the possibility of the sleep disorder classification using ECG only. The SDN can be a useful tool or an alternative screening method based on single-lead ECGs for sleep monitoring and screening.

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