scholarly journals Fine-Grained Image Classification for Crop Disease Based on Attention Mechanism

2020 ◽  
Vol 11 ◽  
Author(s):  
Guofeng Yang ◽  
Yong He ◽  
Yong Yang ◽  
Beibei Xu
Keyword(s):  
Image Classification ◽  
Classification Accuracy ◽  
Attention Mechanism ◽  
Classification Model ◽  
Classification Models ◽  
Fine Grained ◽  
Complex Scenes ◽  
Crop Disease ◽  
Visual Disturbances

Fine-grained image classification is a challenging task because of the difficulty in identifying discriminant features, it is not easy to find the subtle features that fully represent the object. In the fine-grained classification of crop disease, visual disturbances such as light, fog, overlap, and jitter are frequently encountered. To explore the influence of the features of crop leaf images on the classification results, a classification model should focus on the more discriminative regions of the image while improving the classification accuracy of the model in complex scenes. This paper proposes a novel attention mechanism that effectively utilizes the informative regions of an image, and describes the use of transfer learning to quickly construct several fine-grained image classification models of crop disease based on this attention mechanism. This study uses 58,200 crop leaf images as a dataset, including 14 different crops and 37 different categories of healthy/diseased crops. Among them, different diseases of the same crop have strong similarities. The NASNetLarge fine-grained classification model based on the proposed attention mechanism achieves the best classification effect, with an F1 score of up to 93.05%. The results show that the proposed attention mechanism effectively improves the fine-grained classification of crop disease images.

scholarly journals Automatic Detection and Classification of Cough Events Based on Deep Learning

2020 ◽  
Vol 6 (3) ◽  
pp. 322-325
Author(s):  
Seyed Amir Hossein Tabatabaei ◽  
Gabriela Augustinov ◽  
Volker Gross ◽  
Keywan Sohrabi ◽  
Patrick Fischer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Operating Characteristic ◽  
Binary Classification ◽  
Classification Model ◽  
Learning Approach ◽  
Classification Models ◽  
Recording Channels ◽  
Cough Sound

AbstractIn this paper, a deep learning approach for classification of cough sound segments is presented. The architecture of the network is based on a pre-trained network and the spectrogram images of three recording channels have been extracted for the sake of training the network. The classification accuracy based on three recording channels is 92% for a binary classification model and the network converges fast. Two classification models based on binary and multi-class problems are proposed. Relevant classification parameters including the Receiver Operating Characteristic (ROC) curve are reported.

scholarly journals Exploring Misclassification Information for Fine-Grained Image Classification

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4176
Author(s):  
Da-Han Wang ◽  
Wei Zhou ◽  
Jianmin Li ◽  
Yun Wu ◽  
Shunzhi Zhu
Keyword(s):  
Image Classification ◽  
Classification Accuracy ◽  
Experimental Results ◽  
Classification Method ◽  
Public Image ◽  
Classification Models ◽  
Classification Methods ◽  
Fine Grained ◽  
Number Of Classes ◽  
Image Datasets

Fine-grained image classification is a hot topic that has been widely studied recently. Many fine-grained image classification methods ignore misclassification information, which is important to improve classification accuracy. To make use of misclassification information, in this paper, we propose a novel fine-grained image classification method by exploring the misclassification information (FGMI) of prelearned models. For each class, we harvest the confusion information from several prelearned fine-grained image classification models. For one particular class, we select a number of classes which are likely to be misclassified with this class. The images of selected classes are then used to train classifiers. In this way, we can reduce the influence of irrelevant images to some extent. We use the misclassification information for all the classes by training a number of confusion classifiers. The outputs of these trained classifiers are combined to represent images and produce classifications. To evaluate the effectiveness of the proposed FGMI method, we conduct fine-grained classification experiments on several public image datasets. Experimental results prove the usefulness of the proposed method.

scholarly journals An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 249
Author(s):  
Xin Jin ◽  
Yuanwen Zou ◽  
Zhongbing Huang
Keyword(s):  
Cell Cycle ◽  
Deep Learning ◽  
Image Classification ◽  
Classification Accuracy ◽  
Data Augmentation ◽  
Cycle Phase ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

scholarly journals Analytical Comparison of Two Emotion Classification Models Based on Convolutional Neural Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Huiping Jiang ◽  
Demeng Wu ◽  
Rui Jiao ◽  
Zongnan Wang
Keyword(s):  
Wavelet Transform ◽  
Classification Accuracy ◽  
Eeg Signal ◽  
Classification Models ◽  
Emotion Classification ◽  
Common Space ◽  
Average Classification Accuracy ◽  
The Common ◽  
The Brain

Electroencephalography (EEG) is the measurement of neuronal activity in different areas of the brain through the use of electrodes. As EEG signal technology has matured over the years, it has been applied in various methods to EEG emotion recognition, most significantly including the use of convolutional neural network (CNN). However, these methods are still not ideal, and shortcomings have been found in the results of some models of EEG feature extraction and classification. In this study, two CNN models were selected for the extraction and classification of preprocessed data, namely, common spatial patterns- (CSP-) CNN and wavelet transform- (WT-) CNN. Using the CSP-CNN, we first used the common space model to reduce dimensionality and then applied the CNN directly to extract and classify the features of the EEG; while, with the WT-CNN model, we used the wavelet transform to extract EEG features, thereafter applying the CNN for classification. The EEG classification results of these two classification models were subsequently analyzed and compared, with the average classification accuracy of the CSP-CNN model found to be 80.56%, and the average classification accuracy of the WT-CNN model measured to 86.90%. Thus, the findings of this study show that the average classification accuracy of the WT-CNN model was 6.34% higher than that of the CSP-CNN.

scholarly journals RESEARCH ON BRAIN NETWORK CLASSIFICATION METHOD BASED ON INTEGRATED MODEL

2020 ◽  
pp. 1-2
Author(s):  
Zhang- sensen
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Brain Network ◽  
Classification Model ◽  
Comparative Experiment ◽  
Machine Learning Methods ◽  
Healthy Elderly ◽  
Healthy Elderly People ◽  
The Brain

mild cognitive impairment (MCI) is a condition between healthy elderly people and alzheimer's disease (AD). At present, brain network analysis based on machine learning methods can help diagnose MCI. In this paper, the brain network is divided into several subnets based on the shortest path,and the feature vectors of each subnet are extracted and classified. In order to make full use of subnet information, this paper adopts integrated classification model for classification.Each base classification model can predict the classification of a subnet,and the classification results of all subnets are calculated as the classification results of brain network.In order to verify the effectiveness of this method,a brain network of 66 people was constructed and a comparative experiment was carried out.The experimental results show that the classification accuracy of the integrated classification model proposed in this paper is 19% higher than that of SVM,which effectively improves the classification accuracy

scholarly journals Generating Natural Counterfactual Visual Explanations

2020 ◽  
Author(s):  
Wenqi Zhao ◽  
Satoshi Oyama ◽  
Masahito Kurihara
Keyword(s):  
Image Classification ◽  
Classification Accuracy ◽  
Classification Task ◽  
Learning Models ◽  
Generative Adversarial Network ◽  
Fine Grained ◽  
Class A ◽  
Adversarial Network ◽  
Class B ◽  
Machine Learning Models

Counterfactual explanations help users to understand the behaviors of machine learning models by changing the inputs for the existing outputs. For an image classification task, an example counterfactual visual explanation explains: "for an example that belongs to class A, what changes do we need to make to the input so that the output is more inclined to class B." Our research considers changing the attribute description text of class A on the basis of the attributes of class B and generating counterfactual images on the basis of the modified text. We can use the prediction results of the model on counterfactual images to find the attributes that have the greatest effect when the model is predicting classes A and B. We applied our method to a fine-grained image classification dataset and used the generative adversarial network to generate natural counterfactual visual explanations. To evaluate these explanations, we used them to assist crowdsourcing workers in an image classification task. We found that, within a specific range, they improved classification accuracy.

scholarly journals Research on Classification of Fine-Grained Rock Images Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yong Liang ◽  
Qi Cui ◽  
Xing Luo ◽  
Zhisong Xie
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Data Augmentation ◽  
Classification Performance ◽  
Classification Model ◽  
Image Block ◽  
Rock Classification ◽  
Original Algorithm ◽  
Fine Grained ◽  
Low Efficiency

Rock classification is a significant branch of geology which can help understand the formation and evolution of the planet, search for mineral resources, and so on. In traditional methods, rock classification is usually done based on the experience of a professional. However, this method has problems such as low efficiency and susceptibility to subjective factors. Therefore, it is of great significance to establish a simple, fast, and accurate rock classification model. This paper proposes a fine-grained image classification network combining image cutting method and SBV algorithm to improve the classification performance of a small number of fine-grained rock samples. The method uses image cutting to achieve data augmentation without adding additional datasets and uses image block voting scoring to obtain richer complementary information, thereby improving the accuracy of image classification. The classification accuracy of 32 images is 75%, 68.75%, and 75%. The results show that the method proposed in this paper has a significant improvement in the accuracy of image classification, which is 34.375%, 18.75%, and 43.75% higher than that of the original algorithm. It verifies the effectiveness of the algorithm in this paper and at the same time proves that deep learning has great application value in the field of geology.

scholarly journals Research on Automatic Classification Method of Ethnic Music Emotion Based on Machine Learning

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Zijin Wu
Keyword(s):  
Folk Music ◽  
Classification Accuracy ◽  
Fine Tuning ◽  
Classification Model ◽  
Classification Method ◽  
Support Vector ◽  
Ethnic Music ◽  
Huge Impact ◽  
Speed Up

With the development of the country’s economy, there is a flourishing situation in the field of culture and art. However, the diversification of artistic expressions has not brought development to folk music. On the contrary, it brought a huge impact, and some national music even fell into the dilemma of being lost. This article is mainly aimed at the recognition and classification of folk music emotions and finds the model that can make the classification accuracy rate as high as possible. The classification model used in this article is mainly after determining the use of Support Vector Machine (SVM) classification method, a variety of attempts have been made to feature extraction, and good results have been achieved. Explore the Deep Belief Network (DBN) pretraining and reverse fine-tuning process, using DBN to learn the fusion characteristics of music. According to the abstract characteristics learned by them, the recognition and classification of folk music emotions are carried out. The DBN is improved by adding “Dropout” to each Restricted Boltzmann Machine (RBM) and adjusting the increase standard of weight and bias. The improved network can avoid the overfitting problem and speed up the training of the network. Through experiments, it is found that using the fusion features proposed in this paper, through classification, the classification accuracy has been improved.

Reporting Subscore Profiles Using Diagnostic Classification Models in Health Professions Education

2019 ◽  
Vol 43 (3) ◽  
pp. 149-158
Author(s):  
Yoon Soo Park ◽  
Amy Morales ◽  
Linette Ross ◽  
Miguel Paniagua
Keyword(s):  
Large Scale ◽  
Latent Class ◽  
Health Professions ◽  
Health Professions Education ◽  
Diagnostic Classification ◽  
Parameter Estimates ◽  
Classification Models ◽  
Diagnostic Classification Models ◽  
Fine Grained

Learners and educators in the health professions have called for more fine-grained information (subscores) from assessments, beyond a single overall test score. However, due to concerns over reliability, there have been limited uses of subscores in practice. Recent advances in latent class analysis have made contributions in subscore reporting by using diagnostic classification models (DCMs), which allow reliable classification of examinees into fine-grained proficiency levels (subscore profiles). This study examines the innovative and practical application of DCM framework to health professions educational assessments using retrospective large-scale assessment data from the basic and clinical sciences: National Board of Medical Examiners Subject Examinations in pathology ( n = 2,006) and medicine ( n = 2,351). DCMs were fit and analyzed to generate subscores and subscore profiles of examinees. Model fit indices, classification (reliability), and parameter estimates indicated that DCMs had good psychometric properties including consistent classification of examinees into subscore profiles. Results showed a range of useful information including varying levels of subscore distributions. The DCM framework can be a promising approach to report subscores in health professions education. Consistency of classification was high, demonstrating reliable results at fine-grained subscore levels, allowing for targeted and specific feedback to learners.

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