scholarly journals Gastrointestinal Disease Classification in Endoscopic Images Using Attention-Guided Convolutional Neural Networks

2021 ◽  
Vol 11 (23) ◽  
pp. 11136
Author(s):  
Zenebe Markos Lonseko ◽  
Prince Ebenezer Adjei ◽  
Wenju Du ◽  
Chengsi Luo ◽  
Dingcan Hu ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Spatial Information ◽  
Gastrointestinal Disease ◽  
Confusion Matrix ◽  
Automatic Classification ◽  
Classification Performance ◽  
Attention Mechanism ◽  
Disease Classification ◽  
Matrix Analysis

Gastrointestinal (GI) diseases constitute a leading problem in the human digestive system. Consequently, several studies have explored automatic classification of GI diseases as a means of minimizing the burden on clinicians and improving patient outcomes, for both diagnostic and treatment purposes. The challenge in using deep learning-based (DL) approaches, specifically a convolutional neural network (CNN), is that spatial information is not fully utilized due to the inherent mechanism of CNNs. This paper proposes the application of spatial factors in improving classification performance. Specifically, we propose a deep CNN-based spatial attention mechanism for the classification of GI diseases, implemented with encoder–decoder layers. To overcome the data imbalance problem, we adapt data-augmentation techniques. A total of 12,147 multi-sited, multi-diseased GI images, drawn from publicly available and private sources, were used to validate the proposed approach. Furthermore, a five-fold cross-validation approach was adopted to minimize inconsistencies in intra- and inter-class variability and to ensure that results were robustly assessed. Our results, compared with other state-of-the-art models in terms of mean accuracy (ResNet50 = 90.28, GoogLeNet = 91.38, DenseNets = 91.60, and baseline = 92.84), demonstrated better outcomes (Precision = 92.8, Recall = 92.7, F1-score = 92.8, and Accuracy = 93.19). We also implemented t-distributed stochastic neighbor embedding (t–SNE) and confusion matrix analysis techniques for better visualization and performance validation. Overall, the results showed that the attention mechanism improved the automatic classification of multi-sited GI disease images. We validated clinical tests based on the proposed method by overcoming previous limitations, with the goal of improving automatic classification accuracy in future work.

scholarly journals An Artificial Intelligence Framework for Plant Leaf Disease Detection and Classification Using AMBF with GKFCM and GLCM

2021 ◽  
Vol 36 (1) ◽  
pp. 443-450
Author(s):  
Mounika Jammula
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Plant Disease ◽  
Bilateral Filter ◽  
Noise Removal ◽  
Classification Performance ◽  
Disease Classification ◽  
Gaussian Kernel ◽  
Shape Features

As of 2020, the total area planted with crops in India overtook 125.78 million hectares. India is the second biggest organic product maker in the world. Thus, an Indian economy greatly depends on farming products. Nowadays, farmers suffer a drop in production due to a lot of diseases and pests. Thus, to overcome this problem, this article presents the artificial intelligence based deep learning approach for plant disease classification. Initially, the adaptive mean bilateral filter (AMBF) for noise removal and enhancement operations. Then, Gaussian kernel fuzzy C-means (GKFCM) approach is used to segment the effected disease regions. The optimal features from color, texture and shape features are extracted by using GLCM. Finally, Deep learning convolutional neural network (DLCNN) is used for the classification of five class diseases. The segmentation and classification performance of proposed method outperforms as compared with the state of art approaches.

Scene Classification Based on Improved Spatial Partition Model

2014 ◽  
Vol 527 ◽  
pp. 339-342
Author(s):  
Zhi Yuan Liu ◽  
Jin He ◽  
Jin Long Wang ◽  
Fei Zhao
Keyword(s):  
Spatial Information ◽  
Classification Performance ◽  
Image Features ◽  
Natural Scene ◽  
Scene Classification ◽  
Partition Model ◽  
Spatial Partition ◽  
Improved Model ◽  
Local Image

In order to make full use of the spatial information of images in the classification of natural scene, we use the spatial partition model. But mechanically space division caused the abuse of spatial information. So spatial partition model must be properly improved to make the different categories of images were more diversity, so that the classification performance is improved. In addition, to further improve the performance, we use FAN-SIFT as local image features. Experiments made on 8 scenes image dataset and Caltech101 dataset show that the improved model can obtain better classification performance.

scholarly journals Classification of Multi Heart Diseases With Android Based Monitoring System

2020 ◽  
pp. 14-22
Keyword(s):  
Feature Extraction ◽  
Heart Diseases ◽  
Confusion Matrix ◽  
Predictive Coding ◽  
Classification Performance ◽  
Support Vector ◽  
Ecg Signal ◽  
Linear Predictive Coding ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) examination via computer techniques that involve feature extraction, pre-processing and post-processing was implemented due to its significant advantages. Extracting ECG signal standard features that requires high processing operation level was the main focusing point for many studies. In this paper, up to 6 different ECG signal classes are accurately predicted in the absence of ECG feature extraction. The corner stone of the proposed technique in this paper is the Linear predictive coding (LPC) technique that regress and normalize the signal during the pre-processing phase. Prior to the feature extraction using Wavelet energy (WE), a direct Wavelet transform (DWT) is implemented that converted ECG signal to frequency domain. In addition, the dataset was divided into two parts , one for training and the other for testing purposes Which have been classified in this proposed algorithm using support vector machine (SVM). Moreover, using MIT AI2 Companion was developed by MIT Center for Mobile Learning, the classification result was shared to the patient mobile phone that can call the ambulance and send the location in case of serious emergency. Finally, the confusion matrix values are used to measure the proposed classification performance. For 6 different ECG classes, an accuracy ration of about 98.15% was recorded. This ratio became 100% for 3 ECG signal classes and decreases to 97.95% by increasing ECG signal to 7 classes.

Optimal Feature Selection Methods for Chronic Kidney Disease Classification using Intelligent Optimization Algorithms

2020 ◽  
Vol 13 ◽  
Author(s):  
Jerlin Rubini Lambert ◽  
Eswaran Perumal
Keyword(s):  
Chronic Kidney Disease ◽  
Feature Selection ◽  
Kidney Disease ◽  
Classification Performance ◽  
Disease Classification ◽  
Intelligent Optimization Algorithms ◽  
Optimal Feature Selection ◽  
Optimal Feature ◽  
New Framework

Aim: Recently, classification of medical data gives more importance to identify the existence of disease. Background: Numerous classification algorithms for chronic kidney disease (CKD) are developed and produced better classification results. But, the inclusion of different factors in the identification of CKD reduces the effectiveness of the employed classification algorithm. Objective: To overcome this issue, feature selection (FS) approaches are proposed to minimize the computational complexity and also to improve the classification performance in the identification of CKD. Since numerous bio-inspired based FS methodologies are developed, a need arises to examine the feature selection approaches performance of different algorithms on the identification of CKD. Method: This paper proposes a new framework for classification and prediction of CKD. Three feature selection approaches are used namely Ant Colony Optimization (ACO) algorithm, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in the classification process of CKD. Finally, logistic regression (LR) classifier is employed for effective classification. Results: The effectiveness of the ACO-FS, GA-FS and PSO-FS are validated by testing it against a benchmark CKD dataset. Conclusion: The empirical results state that the ACO-FS algorithm performs well and the results reported that the classification performance is improved by the inclusion of feature selection methodologies in CKD classification.

Detection and Classification of Leaf Disease Using Deep Neural Network

2022 ◽  
pp. 51-77
Author(s):  
Meeradevi ◽  
Monica R. Mundada ◽  
Shilpa M.
Keyword(s):  
Neural Network ◽  
Plant Disease ◽  
Early Stage ◽  
Crop Productivity ◽  
Attention Mechanism ◽  
Disease Classification ◽  
Production Plant ◽  
Leaf Disease ◽  
Proposed Model

Modern technologies have improved their application in field of agriculture in order to improve production. Plant diseases are harmful to plant growth, which leads to reduced quality and quantity of crop. Early identification of plant disease will reduce the loss of the crop productivity. So, it is necessary to identify and diagnose the disease at an early stage before it spreads to the entire field. In this chapter, the proposed model uses VGG16 with attention mechanism for leaf disease classification. This model makes use of convolution neural network which consist of convolution block, max pool layer, and fully connected layer with softmax as an activation function. The proposed approach integrates CNN with attention mechanism to focus more on the diseased part of leaf and increase the classification accuracy. The proposed model design is a novel deep learning model to perform the fine tuning in the classification of nine different type of tomato plant disease.

scholarly journals Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network

2019 ◽  
Author(s):  
Li Rui ◽  
Zheng Shunyi ◽  
Duan Chenxi ◽  
Yang Yang ◽  
Wang Xiqi
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Empirical Studies ◽  
The State ◽  
Attention Mechanism ◽  
Spatial Features ◽  
Better Than

In recent years, more and more researchers have gradually paid attention to Hyperspectral Image (HSI) classification. It is significant to implement researches on how to use HSI's sufficient spectral and spatial information to its fullest potential. To capture spectral and spatial features, we propose a Double-Branch Dual-Attention mechanism network (DBDA) for HSI classification in this paper, Two branches aer designed to extract spectral and spatial features separately to reduce the interferences between these two kinds of features. What is more, because distinguishing characteristics exist in the two branches, two types of attention mechanisms are applied in two branches above separately, ensuring to exploit spectral and spatial features more discriminatively. Finally, the extracted features are fused for classification. A series of empirical studies have been conducted on four hyperspectral datasets, and the results show that the proposed method performs better than the state-of-the-art method.

scholarly journals Effect of De-noising by Wavelet Filtering and Data Augmentation by Borderline SMOTE on the Classification of Imbalanced Datasets of Pig Behavior

2021 ◽  
Vol 2 ◽  
Author(s):  
Min Jin ◽  
Chunguang Wang ◽  
Dan Børge Jensen
Keyword(s):  
Data Augmentation ◽  
Science Research ◽  
Classification Performance ◽  
Training Data ◽  
Misclassification Rate ◽  
Feed Forward Neural Network ◽  
Animal Science ◽  
Imbalanced Datasets ◽  
Wavelet Filtering

Classification of imbalanced datasets of animal behavior has been one of the top challenges in the field of animal science. An imbalanced dataset will lead many classification algorithms to being less effective and result in a higher misclassification rate for the minority classes. The aim of this study was to assess a method for addressing the problem of imbalanced datasets of pigs' behavior by using an over-sampling method, namely Borderline-SMOTE. The pigs' activity was measured using a triaxial accelerometer, which was mounted on the back of the pigs. Wavelet filtering and Borderline-SMOTE were both applied as methods to pre-process the dataset. A multilayer feed-forward neural network was trained and validated with 21 input features to classify four pig activities: lying, standing, walking, and exploring. The results showed that wavelet filtering and Borderline-SMOTE both lead to improved performance. Furthermore, Borderline-SMOTE yielded greater improvements in classification performance than an alternative method for balancing the training data, namely random under-sampling, which is commonly used in animal science research. However, the overall performance was not adequate to satisfy the research needs in this field and to address the common but urgent problem of imbalanced behavior dataset.

scholarly journals Automatic Subject Classification of Public Messages in E-government Affairs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Pei Pan ◽  
Yijin Chen
Keyword(s):  
Short Term Memory ◽  
Automatic Classification ◽  
Attention Mechanism ◽  
Classification Model ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Political Inquiry ◽  
Long Short Term Memory

Abstract Public messages on the Internet political inquiry platform rely on manual classification, which has the problems of heavy workload, low efficiency, and high error rate. A Bi-directional long short-term memory (Bi-LSTM) network model based on attention mechanism was proposed in this paper to realize the automatic classification of public messages. Considering the network political inquiry data set provided by the BdRace platform as samples, the Bi-LSTM algorithm is used to strengthen the correlation between the messages before and after the training process, and the semantic attention to important text features is strengthened in combination with the characteristics of attention mechanism. Feature weights are integrated through the full connection layer to carry out classification calculations. The experimental results show that the F1 value of the message classification model proposed here reaches 0.886 and 0.862, respectively, in the data set of long text and short text. Compared with three algorithms of long short-term memory (LSTM), logistic regression, and naive Bayesian, the Bi-LSTM model can achieve better results in the automatic classification of public message subjects.

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