A Study on Improvement of Tomato Disease Classification Performance According to Various Image Augmentation

2021 ◽  
Vol 70 (12) ◽  
pp. 2000-2005
Author(s):  
Hyun-Sik Ham ◽  
Hyun-Chong Cho
Keyword(s):  
Classification Performance ◽  
Disease Classification ◽  
Tomato Disease

scholarly journals Part-Aware Mask-Guided Attention for Thorax Disease Classification

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 653
Author(s):  
Ruihua Zhang ◽  
Fan Yang ◽  
Yan Luo ◽  
Jianyi Liu ◽  
Jinbin Li ◽  
...  
Keyword(s):  
Visual Cues ◽  
Learning Strategy ◽  
Feature Learning ◽  
Classification Performance ◽  
Disease Classification ◽  
Feature Representations ◽  
Chest X Ray ◽  
Global And Local ◽  
Aided Diagnosis ◽  
Single Organ

Thorax disease classification is a challenging task due to complex pathologies and subtle texture changes, etc. It has been extensively studied for years largely because of its wide application in computer-aided diagnosis. Most existing methods directly learn global feature representations from whole Chest X-ray (CXR) images, without considering in depth the richer visual cues lying around informative local regions. Thus, these methods often produce sub-optimal thorax disease classification performance because they ignore the very informative pathological changes around organs. In this paper, we propose a novel Part-Aware Mask-Guided Attention Network (PMGAN) that learns complementary global and local feature representations from all-organ region and multiple single-organ regions simultaneously for thorax disease classification. Specifically, multiple innovative soft attention modules are designed to progressively guide feature learning toward the global informative regions of whole CXR image. A mask-guided attention module is designed to further search for informative regions and visual cues within the all-organ or single-organ images, where attention is elegantly regularized by automatically generated organ masks and without introducing computation during the inference stage. In addition, a multi-task learning strategy is designed, which effectively maximizes the learning of complementary local and global representations. The proposed PMGAN has been evaluated on the ChestX-ray14 dataset and the experimental results demonstrate its superior thorax disease classification performance against the state-of-the-art methods.

Generation and Transformation Invariant Learning for Tomato Disease Classification

2021 ◽  
Author(s):  
Getinet Yilma ◽  
Kumie Gedamu ◽  
Maregu Assefa ◽  
Ariyo Oluwasanmi ◽  
Zhiguang Qin
Keyword(s):  
Disease Classification ◽  
Tomato Disease

scholarly journals Crop Disease Classification on Inadequate Low-Resolution Target Images

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4601
Author(s):  
Juan Wen ◽  
Yangjing Shi ◽  
Xiaoshi Zhou ◽  
Yiming Xue
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Classification Performance ◽  
Disease Classification ◽  
Generative Adversarial Networks ◽  
Low Resolution ◽  
Training Samples ◽  
Crop Disease ◽  
High Resolution Images ◽  
Model Training

Currently, various agricultural image classification tasks are carried out on high-resolution images. However, in some cases, we cannot get enough high-resolution images for classification, which significantly affects classification performance. In this paper, we design a crop disease classification network based on Enhanced Super-Resolution Generative adversarial networks (ESRGAN) when only an insufficient number of low-resolution target images are available. First, ESRGAN is used to recover super-resolution crop images from low-resolution images. Transfer learning is applied in model training to compensate for the lack of training samples. Then, we test the performance of the generated super-resolution images in crop disease classification task. Extensive experiments show that using the fine-tuned ESRGAN model can recover realistic crop information and improve the accuracy of crop disease classification, compared with the other four image super-resolution methods.

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.

scholarly journals Developing Sustainable Classification of Diseases via Deep Learning and Semi-Supervised Learning

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 291 ◽  
Author(s):  
Chunwu Yin ◽  
Zhanbo Chen
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Learning Style ◽  
Classification Performance ◽  
Disease Classification ◽  
Training Data ◽  
Training Procedure ◽  
Training Approach ◽  
Classification Of Diseases ◽  
Deep Forest

Disease classification based on machine learning has become a crucial research topic in the fields of genetics and molecular biology. Generally, disease classification involves a supervised learning style; i.e., it requires a large number of labelled samples to achieve good classification performance. However, in the majority of the cases, labelled samples are hard to obtain, so the amount of training data are limited. However, many unclassified (unlabelled) sequences have been deposited in public databases, which may help the training procedure. This method is called semi-supervised learning and is very useful in many applications. Self-training can be implemented using high- to low-confidence samples to prevent noisy samples from affecting the robustness of semi-supervised learning in the training process. The deep forest method with the hyperparameter settings used in this paper can achieve excellent performance. Therefore, in this work, we propose a novel combined deep learning model and semi-supervised learning with self-training approach to improve the performance in disease classification, which utilizes unlabelled samples to update a mechanism designed to increase the number of high-confidence pseudo-labelled samples. The experimental results show that our proposed model can achieve good performance in disease classification and disease-causing gene identification.

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.

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.

LEX-SVM: EXPLORING THE POTENTIAL OF EXON EXPRESSION PROFILING FOR DISEASE CLASSIFICATION

2011 ◽  
Vol 09 (02) ◽  
pp. 299-316 ◽  
Author(s):  
XIONGYING YUAN ◽  
YI ZHAO ◽  
CHANGNING LIU ◽  
DONGBO BU
Keyword(s):  
Expression Profiling ◽  
Classification Performance ◽  
Disease Classification ◽  
Correlation Structure ◽  
Exon Arrays ◽  
Exon Expression ◽  
New Variant ◽  
Programming Techniques ◽  
Standard Linear ◽  
Linear Svm

Exon expression profiling technologies, including exon arrays and RNA-Seq, measure the abundance of every exon in a gene. Compared with gene expression profiling technologies like 3′ array, exon expression profiling technologies could detect alterations in both transcription and alternative splicing, therefore they are expected to be more sensitive in diagnosis. However, exon expression profiling also brings higher dimension, more redundancy, and significant correlation among features. Ignoring the correlation structure among exons of a gene, a popular classification method like L1-SVM selects exons individually from each gene and thus is vulnerable to noise. To overcome this limitation, we present in this paper a new variant of SVM named Lex-SVM to incorporate correlation structure among exons and known splicing patterns to promote classification performance. Specifically, we construct a new norm, ex-norm, including our prior knowledge on exon correlation structure to regularize the coefficients of a linear SVM. Lex-SVM can be solved efficiently using standard linear programming techniques. The advantage of Lex-SVM is that it can select features group-wisely, force features in a subgroup to take equal weihts and exclude the features that contradict the majority in the subgroup. Experimental results suggest that on exon expression profile, Lex-SVM is more accurate than existing methods. Lex-SVM also generates a more compact model and selects genes more consistently in cross-validation. Unlike L1-SVM selecting only one exon in a gene, Lex-SVM assigns equal weights to as many exons in a gene as possible, lending itself easier for further interpretation.

scholarly journals Deep Learning Framework with ECG Feature-Based Kernels for Heart Disease Classification

2021 ◽  
Vol 27 (1) ◽  
pp. 48-59
Author(s):  
Thanh-Nghia Nguyen ◽  
Thanh-Hai Nguyen
Keyword(s):  
Heart Disease ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Classification Performance ◽  
Disease Classification ◽  
Kernel Size ◽  
S Waves ◽  
Learning Framework ◽  
Good Classification Performance ◽  
Fully Connected

Heart disease classification with high accuracy can support the physician’s correct decision on patients. This paper proposes a kernel size calculation based on P, Q, R, and S waves of one heartbeat to enhance classification accuracy in a deep learning framework. In addition, Electrocardiogram (ECG) signals were filtered using wavelet transform with dmey wavelet, in which the shape of the dmey is closed to that of one heartbeat. With this selected dmey, each heartbeat was standardized with 300 samples for calculation of kernel sizes so that it contains most features in each heartbeat. Therefore, in this research, with 103,459 heart rhythms from the MIT-BIH Arrhythmia Database, the proposed approach for calculation of kernel sizes is effective with seven convolutional layers and other fully connected layers in a Deep Neural Network (DNN). In particular, with five types of heart disease, the result of the high classification accuracy is about 99.4 %. It means that the proposed kernel size calculation in the convolutional layers can achieve good classification performance and it may be developed for classifying different types of disease.

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